Talkulate AI CPQ — alternative to PROS / Conga Smart CPQ? Or buyer validation when your gap is pricing science?
If your gap is pricing science, PROS / Conga Smart CPQ is the right team. If your gap is buyer-facing discovery and validation, Talkulate AI CPQ is.
Same shortlist, different jobs — this page disambiguates pricing operations from buyer natural-language configuration.
PROS / Conga Smart CPQ on the PROS B2B lineage (closed into Conga 2026-02-02) — constraints CPQ, Winter 2026 agents, POM adjacency. For Conga Advantage CPQ and AiMe see /products/talkulate-ai-cpq/comparisons/conga-cpq.
Seller-published comparison · Talkulate AI CPQ team · Reviewed May 2026 · Full disclaimer ↓
Matrix scorecard
Every row below is scored on this buyer-facing job: plain-language request, valid configuration, correct price, and a governed quote your CRM can accept. Row-level answers are in Detailed comparison below; use the Consideration column on each row.
Talk through your catalog with us
How Talkulate AI CPQ sits next to PROS / Conga Smart CPQ without re-platforming — your SKUs, not slides.
New to Talkulate AI CPQ? Start with the product overviewpricing pageROI calculator before reading this comparison.
What should you do?
Choose Talkulate AI CPQ when open-web and messenger buyers need validated configurations — not rep-side price-driver guidance alone.
PROS / Conga Smart CPQ
Choose PROS / Conga Smart CPQ when scheduled price books, rebates, and optimization workflows are the primary ROI.
39 of 39 matrix rows most relevant to this scenario.
Jump to applicable sectionsPricing & timing
| Dimension | Talkulate AI CPQ | PROS / Conga Smart CPQ |
|---|---|---|
| Public list price | €16 000 implementation + €1 500 / month + per-dialog overage; €100 / hour integration; enterprise on-prem option €60 000Full list pricing is on our pricing page. | Post–PROS B2B close packaging is an AE decision (May 2026) — no universal public per-seat grid here |
| Customer effort to live | About 10–15 hours total across 2–3 weeks (typical mid track; min. 2–3 workshops plus embed) | Constraint modeling, POM tracks, and post-close license portability reviews |
| Time to first validated buyer quote | 5 days–6 weeks envelope; typical 3–5 weeks; reference deployment ~5 weeks | Depends on Smart CPQ tenant and packaging clarity after close |
PROS / Conga Smart CPQ publishes no per-seat CPQ list grid as of May 2026; net TCO requires AE quote.
Detailed comparison
PROS / Conga Smart CPQ capabilities cited from public sources reviewed May 2026.
Primary use case
Buyer-facing CPQ slice: discovery through validated configuration, pricing, and proposal — governed quote record may live elsewhere. Catalog and commercial rules can be modeled in Talkulate AI CPQ AI CPQ's database without a Smart CPQ tenant when Talkulate AI CPQ-only is the program.
PROS / Conga Smart CPQ is enterprise rep- and deal-desk-centric CPQ with constraints-based configuration, CRM one-way sync, and suite adjacency to price optimization — discovery is typically guided selling or rep-led, not a native buyer-conversation core.
Primary user persona
Buyer self-serve (full / guided / sales-assisted); partner portals; internal presales paste-in. — Escalation target: < 10% of sessions.
Rep, deal desk, and pricing operations users in Smart CPQ by default. — Headless partner portal and B2B eCommerce are marketing themes — often SI-built on APIs with Conga as rules core; turnkey buyer depth versus custom portal is program-dependent.
Deployment model
Standalone without a Conga Smart CPQ tenant: chat widget, iframe, JS snippet, embedded page, headless API, messengers. Connects to any CRM or ERP stack.
Cloud SaaS on the Conga revenue stack; PROS B2B absorbed 2026-02-02. — Requires Smart CPQ subscription, catalog modeling, and post-close packaging confirmation with Conga.
PROS B2B lineage & M&A scope
No dependency on the Feb 2026 PROS B2B close — deploys as a buyer surface while Conga SKU maps and license portability are clarified.
PROS Holdings B2B business closed into Conga on 2026-02-02; PROS Travel excluded. — Public CPQ URLs now brand Conga Smart CPQ. Orderable SKU map for legacy PROS customers is not published in our review. Not established in PROS / Conga Smart CPQ public CPQ materials reviewed (May 2026); verify with PROS / Conga Smart CPQ.
Entry UX
Plain-language task entry with optional persona routing (B2B technical / B2C / installer / fleet). — Contextual goals, not rigid decision trees.
Guided selling in Smart CPQ UI. Winter 2026 Selling Agent adds rep-side natural-language product find and add-to-quote — buyer NL entry remains a separate build or front door.
Real-time option swaps
Buyer swaps a compatible alternative → Engineer Agent revalidates the full BOM and reprices instantly. Out-of-rule swaps are blocked at the engine level.
Constraints-based logic and formula engine support repricing when rules and UI latency allow; sub-second performance at 10,000+ lines is vendor marketing — tenant benchmarks are buyer-verify.
Self-serve modes
Three built-in modes: full, guided, and sales-assisted; benchmark < 10% escalation.
Headless partner portal and B2B eCommerce are positioning themes — API-first with SI or partner build is common; coexistence with a productized buyer front door such as Talkulate AI CPQ is a typical pattern.
Discovery model
Interviewer Agent: contextual goals — 6–10 questions, 4–8 minutes to validated BOM in reference case.
Guided selling and rep-led discovery in Smart CPQ. Selling Agent (Winter 2026) is rep-side NL catalog search — not documented as buyer configured-line constraint proof. Not established in PROS / Conga Smart CPQ public CPQ materials reviewed (May 2026); verify with PROS / Conga Smart CPQ.
CPQ workflow coverage
Buyer-facing CPQ slice: discovery through validated configuration, pricing, and proposal — governed quote record may live elsewhere. Entry through handoff (CRM, ERP, or downstream Smart CPQ) in one buyer engine when Talkulate AI CPQ-only or as the front layer in coexistence.
Configure, price, quote in Smart CPQ; document outputs; one-way CRM sync; POM price recommendations in quoting when modules are licensed — CLM depth is suite-wide, not fully mapped on this Smart CPQ slice.
Pricing engine depth
Volume tiers, bundles, regional lists, SLA adders, contract rebates, promotions, multi-currency — validated in the same Engineer Agent pass.
Commercial rules in CPQ plus Smart Price Management price books, scheduled generation, rebate spreadsheet upload, and POM optimization workflows when pricing-science modules are in scope.
Pricing science & rebates (POM)
Commercial rules live in the configurator database — integrates to downstream price optimization stacks via scoped hooks, does not ship a parallel POM SKU.
Conga Price Optimization and Management (POM) modules — price optimization, price management, rebate management — marketed with API ties to CRM, ERP, CPQ, and eCommerce. Neural-network optimization examples are vendor-scoped, not generalized proof.
Output formats
Interactive commercial proposal + PDF + per-line reasoning + compatible swaps + branded PDF.
Quote and proposal outputs in Smart CPQ; price-driver transparency for reps on historical deal guidance — buyer-facing per-line reasoning log is not established in PROS-primary CPQ docs reviewed here.
Approval & discount governance
Discount threshold approvals available as add-on — often optional on buyer self-serve where SKU compatibility and price accuracy are the bottleneck.
Historical deal guidance with target price and discount ceiling suggestions; POM-linked recommendations — mature rep pricing-governance adjacency when modules are licensed; buyer paths may defer until revenue risk requires them.
System UI languages
Multi-language requires scoped retraining per language for conversation flows.
Winter 2026 documents eight OOTB system UI languages (English, French, Spanish, German, Italian, Portuguese, Chinese, Polish) — vendor-maintained for system UI and error messages.
Validation method
Deterministic Engineer Agent against live PostgreSQL via MCP — no document retrieval for core compatibility.
Constraints-based configuration engine (marketing) plus formula engine and quote multi-selection data types (Winter 2026 release notes) — rules-driven, not LLM-guesswork for core quoting.
Invalid configuration handling
Engineer Agent blocks incompatible combinations with per-line reasoning before handoff. — In one published hardware reference (~3,400 SKUs), standard BOMs no longer required a separate engineering review after catalog-backed validation.
Constraint and formula logic block disallowed combinations when rules are complete; gaps are admin modeling work. Selling Agent search does not replace constraint proof for buyer sessions.
Winter 2026 AI agents
LLM only in Interviewer Agent; Engineer Agent is non-generative for configuration choices.
Pricing Agent (anomaly alerts), Selling Agent (NL product find on quotes), Operations Agent (data-loading troubleshooting) — rep and ops surfaces with chat history and credit consumption tracking; GA and data residency are buyer-verify.
Dual-agent architecture
Interviewer and Engineer fully separated; Engineer only runs allowlisted DB queries.
Smart CPQ core is rules- and constraint-driven — not LLM-fronted for configuration. Winter agents are additive assistance layers.
Catalog source connectors
PostgreSQL, MySQL, MSSQL, SAP, NetSuite, Dynamics, Salesforce CPQ, Excel, PDF specs, XML, REST, file drops.
Smart CPQ catalog plus one-way CRM sync to Salesforce or Dynamics (Winter 2026); API-first ERP posture in marketing — SAP S/4HANA and Dynamics F&O demo claims need production connector proof.
Per-line audit trail
Per-line reasoning: which constraint triggered each selection — buyer and rep facing.
Price-driver transparency for reps on historical guidance — buyer-facing configured-line reasoning comparable to Talkulate AI CPQ is not documented in PROS-primary CPQ sources.
Observability & tracing
Langfuse per session (Interviewer and Engineer traces separately); rate limits on chat and overview endpoints.
Agent credit consumption tracking noted in Winter 2026 release notes; LLM trace layer for buyer sessions not established in public Smart CPQ materials reviewed here.
Embed channels
Widget, iframe (~10 min deploy), JS snippet, embedded page, headless API, internal-tool mode — productized buyer shell without building on Smart CPQ APIs first.
API-first Smart CPQ with headless partner and eCommerce positioning — SI or partner builds the external shell on Conga rules; many programs pair a custom or Talkulate AI CPQ-class front door with Smart CPQ as quote engine.
Async messenger channels
WhatsApp, Telegram, Teams, Slack, Messenger, LinkedIn — same validation engine, scoped per channel.
Messenger-native CPQ intake is not documented as a standard Smart CPQ feature. — Not established in PROS / Conga Smart CPQ public CPQ materials reviewed (May 2026); verify with PROS / Conga Smart CPQ.
Time to production buyer surface
Envelope 5 days–6 weeks; typical 3–5 weeks; reference case 5 weeks.
Smart CPQ modeling plus optional POM, rebate, and price-book tracks — calendar lengthens with pricing-science scope and post-close SKU clarity.
Customer effort
10–15 hours over 2–3 weeks (vendor claim for standard deployment).
Heavy catalog modeling, CRM sync mapping, pricing-science parallel workstreams, and M&A packaging reviews — effort scales with module scope and SI availability.
"No structured catalog" path
Data structuring service ($3,450–$17,250 one-time) for Excel, PDF, and tribal knowledge.
Catalog and constraint modeling are admin- and partner-scoped; no public fixed-fee structuring SKU on Smart CPQ pages reviewed here.
Verticals
Eight first-class vertical packs on one engine.
Complex B2B and manufacturing positioning on Smart CPQ surfaces — partner-led verticalization, not eight named Talkulate AI CPQ-style packs.
Error mode
Refuse: incompatible combinations are blocked before the buyer accepts output; the engine does not publish out-of-rule builds from document similarity.
Constraint engine blocks disallowed combinations when modeled; incomplete rules can produce plausible but incorrect quotes — remediation is modeling, not swapping the CPQ UI.
Regulatory constraint encoding
CE, FDA, UL, NSF, RoHS, and similar rules encodable in validation engine.
Implementation-dependent in constraint and formula rules — depth depends on SI and customer team.
Anti-prompt-injection
Engineer Agent allowlisted queries only; vendor does not need internal margin data.
Smart CPQ core quoting is not LLM-fronted for configuration; Winter agents are separate surfaces — confirm agent data posture in DPA.
GDPR / PII posture
DPA; no training on client catalog or conversations; data minimization.
PROS B2B–specific SOC artifacts post-close versus Conga Subscription Services legal exhibit were not mapped in this publication pass — verify with Conga.
Multi-tenant isolation & hosting
Cloud AWS/Azure EU regions; per-tenant DB. — On-prem: $69,000 enterprise license.
SaaS posture on Conga revenue stack; PROS-specific residency and isolation details post-close are buyer-verify.
Built-in analytics
Conversation funnel analytics and demand-sensing export.
Conversation funnel analytics for buyer sessions are not established in Smart CPQ-primary docs reviewed here.
Quote cycle (reference metrics)
See the reference deployment band below.
Vendor customer stories on Smart CPQ pages are marketing — no matched public study on equivalent buyer self-serve catalog complexity in this pass.
First-pass accuracy
See the reference deployment band below.
No matched public study on equivalent catalog complexity in this publication pass.
Quote capacity
See the reference deployment band below.
Buyer-facing self-serve capacity uplift is not published in Smart CPQ materials reviewed here. — Not established in PROS / Conga Smart CPQ public CPQ materials reviewed (May 2026); verify with PROS / Conga Smart CPQ.
Conversion uplift
A separate buyer-facing pilot (not the server-reseller reference case) reported better web conversion and fewer “waiting for a quote” drop-offs versus that pilot’s own baseline — treat as directional; measure on your traffic.
Conversion uplift from external Smart CPQ UX is not published in marketing materials reviewed here. — Not established in PROS / Conga Smart CPQ public CPQ materials reviewed (May 2026); verify with PROS / Conga Smart CPQ.
RFQ unit economics
Illustrative unit economics only (not a price quote for your tenant): manual complex RFQs are often cited around $230–$460 versus automated self-serve often cited up to about $12 per RFQ at volume — use Talkulate AI CPQ pricing and ROI tools for your case.
No public per-seat list grid for Smart CPQ or POM in our review — contact Conga for a quote. Stale aggregator bands are not used as fact on this page.
Pricing model
Implementation ($18,400) + monthly ($1,725, 600 dialogs) + per-dialog overage + integration ($115 / hour). Enterprise on-prem: $69,000.
PROS / Conga Smart CPQ and POM: contact sales — no public list grid located in our May 2026 review. Packaging merges with Conga bundles post-close; net TCO requires AE validation.
| Criterion | Talkulate AI CPQ | PROS / Conga Smart CPQ | Consideration |
|---|---|---|---|
| Posture | |||
| Primary use case | Buyer-facing CPQ slice: discovery through validated configuration, pricing, and proposal — governed quote record may live elsewhere. Catalog and commercial rules can be modeled in Talkulate AI CPQ AI CPQ's database without a Smart CPQ tenant when Talkulate AI CPQ-only is the program. | PROS / Conga Smart CPQ is enterprise rep- and deal-desk-centric CPQ with constraints-based configuration, CRM one-way sync, and suite adjacency to price optimization — discovery is typically guided selling or rep-led, not a native buyer-conversation core. | Both, narrow seam |
| Primary user persona | Buyer self-serve (full / guided / sales-assisted); partner portals; internal presales paste-in. — Escalation target: < 10% of sessions. | Rep, deal desk, and pricing operations users in Smart CPQ by default. — Headless partner portal and B2B eCommerce are marketing themes — often SI-built on APIs with Conga as rules core; turnkey buyer depth versus custom portal is program-dependent. | Depends on your gap |
| Deployment model | Standalone without a Conga Smart CPQ tenant: chat widget, iframe, JS snippet, embedded page, headless API, messengers. Connects to any CRM or ERP stack. | Cloud SaaS on the Conga revenue stack; PROS B2B absorbed 2026-02-02. — Requires Smart CPQ subscription, catalog modeling, and post-close packaging confirmation with Conga. | Talkulate AI CPQ job |
| PROS B2B lineage & M&A scope | No dependency on the Feb 2026 PROS B2B close — deploys as a buyer surface while Conga SKU maps and license portability are clarified. | PROS Holdings B2B business closed into Conga on 2026-02-02; PROS Travel excluded. — Public CPQ URLs now brand Conga Smart CPQ. Orderable SKU map for legacy PROS customers is not published in our review. Not established in PROS / Conga Smart CPQ public CPQ materials reviewed (May 2026); verify with PROS / Conga Smart CPQ. | Depends on your gap |
| Entry UX | Plain-language task entry with optional persona routing (B2B technical / B2C / installer / fleet). — Contextual goals, not rigid decision trees. | Guided selling in Smart CPQ UI. Winter 2026 Selling Agent adds rep-side natural-language product find and add-to-quote — buyer NL entry remains a separate build or front door. | Talkulate AI CPQ job |
| Real-time option swaps | Buyer swaps a compatible alternative → Engineer Agent revalidates the full BOM and reprices instantly. Out-of-rule swaps are blocked at the engine level. | Constraints-based logic and formula engine support repricing when rules and UI latency allow; sub-second performance at 10,000+ lines is vendor marketing — tenant benchmarks are buyer-verify. | Talkulate AI CPQ job |
| Self-serve modes | Three built-in modes: full, guided, and sales-assisted; benchmark < 10% escalation. | Headless partner portal and B2B eCommerce are positioning themes — API-first with SI or partner build is common; coexistence with a productized buyer front door such as Talkulate AI CPQ is a typical pattern. | Both, narrow seam |
| Discovery & pricing | |||
| Discovery model | Interviewer Agent: contextual goals — 6–10 questions, 4–8 minutes to validated BOM in reference case. | Guided selling and rep-led discovery in Smart CPQ. Selling Agent (Winter 2026) is rep-side NL catalog search — not documented as buyer configured-line constraint proof. Not established in PROS / Conga Smart CPQ public CPQ materials reviewed (May 2026); verify with PROS / Conga Smart CPQ. | Talkulate AI CPQ job |
| CPQ workflow coverage | Buyer-facing CPQ slice: discovery through validated configuration, pricing, and proposal — governed quote record may live elsewhere. Entry through handoff (CRM, ERP, or downstream Smart CPQ) in one buyer engine when Talkulate AI CPQ-only or as the front layer in coexistence. | Configure, price, quote in Smart CPQ; document outputs; one-way CRM sync; POM price recommendations in quoting when modules are licensed — CLM depth is suite-wide, not fully mapped on this Smart CPQ slice. | Both, narrow seam |
| Pricing engine depth | Volume tiers, bundles, regional lists, SLA adders, contract rebates, promotions, multi-currency — validated in the same Engineer Agent pass. | Commercial rules in CPQ plus Smart Price Management price books, scheduled generation, rebate spreadsheet upload, and POM optimization workflows when pricing-science modules are in scope. | PROS / Conga Smart CPQ job |
| Pricing science & rebates (POM) | Commercial rules live in the configurator database — integrates to downstream price optimization stacks via scoped hooks, does not ship a parallel POM SKU. | Conga Price Optimization and Management (POM) modules — price optimization, price management, rebate management — marketed with API ties to CRM, ERP, CPQ, and eCommerce. Neural-network optimization examples are vendor-scoped, not generalized proof. | PROS / Conga Smart CPQ job |
| Output formats | Interactive commercial proposal + PDF + per-line reasoning + compatible swaps + branded PDF. | Quote and proposal outputs in Smart CPQ; price-driver transparency for reps on historical deal guidance — buyer-facing per-line reasoning log is not established in PROS-primary CPQ docs reviewed here. | Talkulate AI CPQ job |
| Approval & discount governance | Discount threshold approvals available as add-on — often optional on buyer self-serve where SKU compatibility and price accuracy are the bottleneck. | Historical deal guidance with target price and discount ceiling suggestions; POM-linked recommendations — mature rep pricing-governance adjacency when modules are licensed; buyer paths may defer until revenue risk requires them. | Depends on your gap |
| System UI languages | Multi-language requires scoped retraining per language for conversation flows. | Winter 2026 documents eight OOTB system UI languages (English, French, Spanish, German, Italian, Portuguese, Chinese, Polish) — vendor-maintained for system UI and error messages. | Depends on your gap |
| Validation | |||
| Validation method | Deterministic Engineer Agent against live PostgreSQL via MCP — no document retrieval for core compatibility. | Constraints-based configuration engine (marketing) plus formula engine and quote multi-selection data types (Winter 2026 release notes) — rules-driven, not LLM-guesswork for core quoting. | Both, narrow seam |
| Invalid configuration handling | Engineer Agent blocks incompatible combinations with per-line reasoning before handoff. — In one published hardware reference (~3,400 SKUs), standard BOMs no longer required a separate engineering review after catalog-backed validation. | Constraint and formula logic block disallowed combinations when rules are complete; gaps are admin modeling work. Selling Agent search does not replace constraint proof for buyer sessions. | Depends on your gap |
| Winter 2026 AI agents | LLM only in Interviewer Agent; Engineer Agent is non-generative for configuration choices. | Pricing Agent (anomaly alerts), Selling Agent (NL product find on quotes), Operations Agent (data-loading troubleshooting) — rep and ops surfaces with chat history and credit consumption tracking; GA and data residency are buyer-verify. | Both, narrow seam |
| Dual-agent architecture | Interviewer and Engineer fully separated; Engineer only runs allowlisted DB queries. | Smart CPQ core is rules- and constraint-driven — not LLM-fronted for configuration. Winter agents are additive assistance layers. | Talkulate AI CPQ job |
| Catalog source connectors | PostgreSQL, MySQL, MSSQL, SAP, NetSuite, Dynamics, Salesforce CPQ, Excel, PDF specs, XML, REST, file drops. | Smart CPQ catalog plus one-way CRM sync to Salesforce or Dynamics (Winter 2026); API-first ERP posture in marketing — SAP S/4HANA and Dynamics F&O demo claims need production connector proof. | Depends on your gap |
| Per-line audit trail | Per-line reasoning: which constraint triggered each selection — buyer and rep facing. | Price-driver transparency for reps on historical guidance — buyer-facing configured-line reasoning comparable to Talkulate AI CPQ is not documented in PROS-primary CPQ sources. | Talkulate AI CPQ job |
| Observability & tracing | Langfuse per session (Interviewer and Engineer traces separately); rate limits on chat and overview endpoints. | Agent credit consumption tracking noted in Winter 2026 release notes; LLM trace layer for buyer sessions not established in public Smart CPQ materials reviewed here. | Talkulate AI CPQ job |
| Channels & time to value | |||
| Embed channels | Widget, iframe (~10 min deploy), JS snippet, embedded page, headless API, internal-tool mode — productized buyer shell without building on Smart CPQ APIs first. | API-first Smart CPQ with headless partner and eCommerce positioning — SI or partner builds the external shell on Conga rules; many programs pair a custom or Talkulate AI CPQ-class front door with Smart CPQ as quote engine. | Both, narrow seam |
| Async messenger channels | WhatsApp, Telegram, Teams, Slack, Messenger, LinkedIn — same validation engine, scoped per channel. | Messenger-native CPQ intake is not documented as a standard Smart CPQ feature. — Not established in PROS / Conga Smart CPQ public CPQ materials reviewed (May 2026); verify with PROS / Conga Smart CPQ. | Talkulate AI CPQ job |
| Time to production buyer surface | Envelope 5 days–6 weeks; typical 3–5 weeks; reference case 5 weeks. | Smart CPQ modeling plus optional POM, rebate, and price-book tracks — calendar lengthens with pricing-science scope and post-close SKU clarity. | Talkulate AI CPQ job |
| Customer effort | 10–15 hours over 2–3 weeks (vendor claim for standard deployment). | Heavy catalog modeling, CRM sync mapping, pricing-science parallel workstreams, and M&A packaging reviews — effort scales with module scope and SI availability. | Talkulate AI CPQ job |
| "No structured catalog" path | Data structuring service ($3,450–$17,250 one-time) for Excel, PDF, and tribal knowledge. | Catalog and constraint modeling are admin- and partner-scoped; no public fixed-fee structuring SKU on Smart CPQ pages reviewed here. | Depends on your gap |
| Verticals | Eight first-class vertical packs on one engine. | Complex B2B and manufacturing positioning on Smart CPQ surfaces — partner-led verticalization, not eight named Talkulate AI CPQ-style packs. | Talkulate AI CPQ job |
| Security & governance | |||
| Error mode | Refuse: incompatible combinations are blocked before the buyer accepts output; the engine does not publish out-of-rule builds from document similarity. | Constraint engine blocks disallowed combinations when modeled; incomplete rules can produce plausible but incorrect quotes — remediation is modeling, not swapping the CPQ UI. | Depends on your gap |
| Regulatory constraint encoding | CE, FDA, UL, NSF, RoHS, and similar rules encodable in validation engine. | Implementation-dependent in constraint and formula rules — depth depends on SI and customer team. | Depends on your gap |
| Anti-prompt-injection | Engineer Agent allowlisted queries only; vendor does not need internal margin data. | Smart CPQ core quoting is not LLM-fronted for configuration; Winter agents are separate surfaces — confirm agent data posture in DPA. | Talkulate AI CPQ job |
| GDPR / PII posture | DPA; no training on client catalog or conversations; data minimization. | PROS B2B–specific SOC artifacts post-close versus Conga Subscription Services legal exhibit were not mapped in this publication pass — verify with Conga. | Depends on your gap |
| Multi-tenant isolation & hosting | Cloud AWS/Azure EU regions; per-tenant DB. — On-prem: $69,000 enterprise license. | SaaS posture on Conga revenue stack; PROS-specific residency and isolation details post-close are buyer-verify. | Depends on your gap |
| Built-in analytics | Conversation funnel analytics and demand-sensing export. | Conversation funnel analytics for buyer sessions are not established in Smart CPQ-primary docs reviewed here. | Talkulate AI CPQ job |
| Outcomes & commercial | |||
| Quote cycle (reference metrics) | See the reference deployment band below. | Vendor customer stories on Smart CPQ pages are marketing — no matched public study on equivalent buyer self-serve catalog complexity in this pass. | Talkulate AI CPQ job |
| First-pass accuracy | See the reference deployment band below. | No matched public study on equivalent catalog complexity in this publication pass. | Talkulate AI CPQ job |
| Quote capacity | See the reference deployment band below. | Buyer-facing self-serve capacity uplift is not published in Smart CPQ materials reviewed here. — Not established in PROS / Conga Smart CPQ public CPQ materials reviewed (May 2026); verify with PROS / Conga Smart CPQ. | Talkulate AI CPQ job |
| Conversion uplift | A separate buyer-facing pilot (not the server-reseller reference case) reported better web conversion and fewer “waiting for a quote” drop-offs versus that pilot’s own baseline — treat as directional; measure on your traffic. | Conversion uplift from external Smart CPQ UX is not published in marketing materials reviewed here. — Not established in PROS / Conga Smart CPQ public CPQ materials reviewed (May 2026); verify with PROS / Conga Smart CPQ. | Talkulate AI CPQ job |
| RFQ unit economics | Illustrative unit economics only (not a price quote for your tenant): manual complex RFQs are often cited around $230–$460 versus automated self-serve often cited up to about $12 per RFQ at volume — use Talkulate AI CPQ pricing and ROI tools for your case. | No public per-seat list grid for Smart CPQ or POM in our review — contact Conga for a quote. Stale aggregator bands are not used as fact on this page. | Depends on your gap |
| Pricing model | Implementation ($18,400) + monthly ($1,725, 600 dialogs) + per-dialog overage + integration ($115 / hour). Enterprise on-prem: $69,000. | PROS / Conga Smart CPQ and POM: contact sales — no public list grid located in our May 2026 review. Packaging merges with Conga bundles post-close; net TCO requires AE validation. | Depends on your gap |
Discuss implementing Talkulate AI CPQ
Bring your catalog and buyer channels — we map where Talkulate AI CPQ fits your quoting job, rollout path, and timeline without re-platforming.
Concrete on your SKUs: integration seams, validation scope, and a realistic weeks-to-live plan — not a generic demo.
Where Talkulate AI CPQ's job sits in a real deployment
Reference deployment
North American IT distributor (~3 400 SKUs)
~15 min vs 1–2 days to a validated standard quote
Complex hardware catalog; baseline was rep- and engineering-assisted quoting before a buyer-facing validation layer went live. Results vary with catalog size, channels, and downstream CPQ handoff.
Three projections of one reference deployment — not three separate customers.
Quote cycle (standard configs)
About 1–2 days → about 15 minutes
First-pass accuracy
Mandatory engineering review removed on standard BOMs after catalog-backed validation
Quote capacity
More validated quotes per week; ~22 hours/week freed across three engineers in the same program
Documented case study — full write-up, metrics, and implementation scope.
Sources & methodology
We extracted vendor capability statements from public sources in May 2026. Where a buyer-facing dimension was not found in CPQ-primary materials, we mark the cell accordingly. Talkulate AI CPQ outcome metrics come from our own deployments and pilots, with sample size disclosed. Counts in the hero scorecard are exact tallies from this page's matrix.
FAQ
Which gap do I actually have — pricing science or buyer-facing validation (May 2026)?
If your gap is pricing science, margin optimization, scheduled price books, and rebate operations, PROS / Conga Smart CPQ is the right team. If your gap is buyer natural-language discovery and deterministic configuration validation on a complex catalog, Talkulate AI CPQ is. This page splits matrix rows by job — it is not a feature fight.
How does public pricing compare for Talkulate AI CPQ and PROS / Conga Smart CPQ (May 2026)?
Talkulate AI CPQ publishes reference fees on the pricing page: $18,400 one-time cloud implementation plus $1,725 / month (600 dialogs included). PROS / Conga Smart CPQ and Conga POM: no public per-seat list grid in our review — contact Conga for a quote. Do not treat stale third-party aggregator bands as fact, especially post-close.
Can Talkulate AI CPQ replace PROS / Conga Smart CPQ, work alongside it, or run Talkulate AI CPQ-only?
Talkulate AI CPQ-only is a valid path for buyer-surface programs: catalog and rules in Talkulate AI CPQ AI CPQ's database, validated quotes and proposals without a Smart CPQ tenant. Full rip-and-replace of Conga quote governance, POM programs, and rep-side deal guidance on Smart CPQ tenants is a separate migration decision. Coexistence — Talkulate AI CPQ for external discovery and rule-safe configuration, PROS / Conga Smart CPQ for governed quotes and CRM sync — is common when both jobs matter.
Can we pilot coexistence with PROS / Conga Smart CPQ — and keep that model long term?
Yes — many teams may run both. Talkulate AI CPQ runs on your website, embed, or messengers; validated BOMs, priced lines, attributes, and optional transcripts are mapped into PROS / Conga Smart CPQ via scoped APIs or custom connectors built in implementation. Confirm post-close SKU entitlements, CRM one-way sync rules, and which quote objects receive the handoff before you rely on coexistence in procurement.
Where can I see your reference deployment beyond this page?
See case studies on the site for the North American IT distributor program cited in the reference deployment band below the matrix. Named testimony is anonymised at customer request — NDA reference call available on request.
How does this page relate to Conga CPQ (Advantage) and Conga AiMe?
AiMe and Conga Advantage CPQ (Salesforce-native line, Cart APIs, quote-to-contract on Advantage Platform) are covered on /products/talkulate-ai-cpq/comparisons/conga-cpq. This page covers Smart CPQ on the PROS B2B lineage and Winter 2026 platform agents — different product line and AI scope.
How is Conga Advantage CPQ different from this page?
This page covers the Smart CPQ line on the PROS B2B lineage absorbed into Conga in February 2026 — constraints-based CPQ, Winter 2026 platform features, and POM adjacency. Conga Advantage CPQ is the Salesforce-native CPQ line (Cart APIs, Salesforce quoting integration). For Advantage scope, see the full comparison at /products/talkulate-ai-cpq/comparisons/conga-cpq.
What are Winter 2026 Selling, Pricing, and Operations agents vs Talkulate AI CPQ?
Selling Agent helps reps find and add products via natural language. Pricing Agent surfaces pricing anomalies. Operations Agent assists with data-loading issues. They are rep- or ops-side assistance with credit tracking in release notes — not buyer natural-language configuration with deterministic configured-line validation. Talkulate AI CPQ uses an LLM only in the Interviewer Agent; the Engineer Agent validates every configured line against your catalog for buyer-facing sessions.
What changed when PROS B2B joined Conga (February 2026)?
Conga completed acquisition of the PROS Holdings B2B business on 2026-02-02; PROS Travel remains separate. Public CPQ marketing now brands Conga Smart CPQ. Orderable SKU maps, license portability, and roadmap merge details for legacy PROS customers are not fully public — confirm packaging with Conga before you model TCO or renewal terms.
What outcome metrics does Talkulate AI CPQ cite?
On this page Talkulate AI CPQ cites (1) a reference deployment on a ~3,400-SKU hardware catalog — about 1–2 days to about 15 minutes for standard validated quotes, engineering review removed on standard BOMs, about five weeks to production; and (2) a separate web pilot with conversion and wait-time gains versus that pilot's baseline only. Neither program measured Smart CPQ or post-PROS-close packaging. If you coexist, budget integration and Conga modeling separately from those Talkulate AI CPQ numbers — see the reference deployment band below the matrix. Do not treat Talkulate AI CPQ metrics as PROS / Conga Smart CPQ or POM benchmarks.
Disclaimer
Talkulate AI CPQ (published by R[AI]SING SUN, Raising Sun s.r.o.) publishes this page to help buyers compare CPQ and guided-selling options. Talkulate AI CPQ is our product; this is a seller-published comparison, not an independent third-party review. We are not affiliated with Conga, Inc..
Comparison scope: this page addresses one job sequence — a buyer or partner request in plain language, validation against a live catalog, commercial pricing, a governed quote artifact, and handoff to CRM or ERP — not a complete review of every Conga, Inc. product, roadmap, integration, or total cost of ownership.
Conga, Inc. capabilities are summarized from publicly available documentation reviewed May 2026. Product scope, packaging, pricing, and roadmaps change — verify current details directly with Conga, Inc. before procurement or security decisions.
Detailed comparison rows and Consideration labels (including Talkulate AI CPQ, Conga, Inc., Coexistence, and Program-dependent) reflect Talkulate’s good-faith assessment for that criterion on this scope. They are not independent third-party scores, benchmarks, or guarantees of fit for your tenant or program.
Talkulate outcome metrics on this page (for example conversion, quote cycle, accuracy, or capacity) come from referenced deployments and pilots unless stated otherwise; your results depend on catalog complexity, baselines, and implementation. Conga, Inc. outcome claims appear only when supported by eligible public materials or are clearly marked as not directly comparable.
This page is for general information only. It does not constitute legal, financial, procurement, or professional advice. Engage qualified advisors for contracts, compliance, security review, and vendor selection.
Third-party product names are trademarks of their respective owners. Sources are listed below without links to competitor-owned websites. Community forum posts are not used as primary evidence on this page.
If you believe a statement is inaccurate or outdated, contact [email protected] with the page URL, the section or table row, and a citation to current public documentation. We review and correct promptly.
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