Workshop asks "will it fit?" Your manager spends 30 minutes checking. Competitor already shipped
Auto parts distributors lose deals not on price — on response time. Every fitment check that goes through a manager is a race you might lose. The workshop in the bay doesn't wait.
30 min
Average manual fitment check — per request
10 min
Fitment validated + quote ready with configurator
0
Wrong parts when fit is checked against your catalog
Workshop sends a fitment request. Manager cross-references vehicle, engine variant, stock. 25–40 minutes if nothing goes wrong. Meanwhile the workshop called two other distributors.
02
The first answer wins
The distributor who answered first — with a confirmed fit and a price — got the order. You replied an hour later. "Already sorted, thanks."
03
The wrong part
When the check is rushed — wrong part ships. Return at your cost. Credit note. Apology. 71% of customers who get a wrong part never order from that supplier again.
The fix
VIN or year/make/model. Fit validated against your catalog. Quote in 10 minutes — manager handles exceptions only.
Most fitment requests never become quotes. Out of 3,000 monthly parts lookups:
3,000
MONTHLY PARTS LOOKUPS
210
2,790 SELF-SERVED OR EXITED (93%)
"WILL IT FIT?" REQUESTS SENT
72
138 GAVE UP — WAIT TOO LONG (66%)
MANAGER CHECKING FITMENT
24
48 STILL IN QUEUE
QUOTES SENT
End-to-end conversion
0.80%
MOST LOST OR DELAYED — SLOW RESPONSE OR NO SELF-SERVICE
What happens when fit is validated before the quote leaves your system
Same request. Same catalog. Now you answer first — and correctly:
10 minutes, not half a day
Year/make/model or VIN. Fit validated. Quote ready. Manager handles exceptions, not routine fitment checks.
Zero wrong parts
Applicability checked automatically against your catalog. No returns for wrong fit. Margins stay intact.
Scale without hiring
One configurator handles hundreds of requests. Capacity grows; headcount doesn't.
Bigger orders
Configurator suggests related parts for the same job. Filter → oil → hoses. One quote, one order.
How others solve it vs. us
You have a catalog. The pain is fitment speed and accuracy
Auto parts have the highest return rate in retail — 19.4%. Wrong fitment is the main cause. 71% of customers who get a wrong part never order from that supplier again.
Fitment lookup tools (VIN/YMM search)
You get fit data — not a full quote, upsell, or qualification in one flow. Manager still has to build the quote manually.
Fit + quote + related parts in one conversation. VIN or year/make/model. One flow from "will it fit?" to validated quote.
Fitment tables, Excel cross-references
Manager digs through the catalog. 25–40 min per request. Doesn't scale under volume.
Conversation-based. Fit validated automatically, wrong parts excluded. Quote in 10 minutes.
"Contact us for fitment"
No self-service. Hard capacity ceiling. Competitor answers while you're still checking.
Self-service quote with validated fit. Manager handles exceptions and complex cases only.
Generic product configurator with dropdowns
50K SKUs in dropdowns don't work for fitment. Customer can't navigate it.
Conversation. Customer describes the vehicle and job — configurator returns validated options. No dropdown maze.
See fitment-to-quote in action
Workshop describes the vehicle and job. Configurator returns a validated quote. Demo run time: ~2 minutes.
Fitment check time
30 min
0 min
Wrong parts shipped
manual
0
Repeat business after wrong fit
29%
validated
Industry benchmarks; your results depend on catalog coverage and request mix.
ROICalc
Get your ROI estimate
See payback for your quote volume. How many leads you lose today, and what changes when the configurator handles first touch.
Loading ROI calculator
What does AI CPQ cost?
Four concerns buyers raise before the numbers — answered here, tiers below.
Too expensive?
One full-time quote specialist ≈ $60,000–90,000/year loaded. Talkulate: implementation + monthly — break-even in a handful of deals you lose to speed or wrong-fit quotes.
Catalog not ready?
Phase 1 includes database construction and population. Data structuring is our job, not yours.
5–10 days too long?
5 days with a clean catalog export. Larger catalogs with CRM integrations: timeline after a free data audit.
Already have CPQ?
Talkulate sits upstream of CPQ — buyer discovery and validation before your rep opens the catalog. We do not replace approval workflows. Compare with incumbent CPQ vendors
Close 3× more deals 10× faster
Turn complex product configuration into a 15-minute guided flow that generates qualified quotes automatically.
~5–10
closed deals to break even
3–5×
conversion rate lift
80%
less presales workload
minutes
RFQ turnaround, not days
Run the ROI calculator — plug in deal size and presales load to see whether the numbers work for you (same page, below).
AI-guided product selection
24/7, no waiting days for a callback
Built-in configuration logic
zero config errors from oversight
Instant quote & PDF generation
close while buyer intent is hot
CRM / website integration
qualified leads sync to your pipeline
White-label customer experience
native on your site, not a third-party widget
Standard Deployment
Cloud deployment. Most common path for rapid market entry.
$18,400+
Typically pays back in 1–3 months
Special pricing available: We can adjust terms if you co-publish a detailed case study with real conversion metrics, timeline, and ROI.
Phase 1: Implementation
Direct website integration (Chat or API)
Business logic customization
System calibration
Database construction & population services
Then: Monthly Infrastructure
$1,725+/month
Managed cloud hosting
Updates & maintenance included
Scales with request volume
Enterprise On-Premise
One-time license
On request
For strict data sovereignty requirements. Full control, deployed on your infrastructure.
Detailed pricing. Full line-item breakdown for Standard and On-Premise. View full breakdown →
Thinking it’s pricey?
One presales engineer costs more per year. AI CPQ cost less.
Don’t need everything?
We’ll map the minimum viable implementation and price it accordingly.
FAQ
Common questions for this industry. The full product FAQ covers implementation, security, and pricing in depth.
We have 50,000 SKUs. How does the configurator know which part fits which vehicle?+−
You load your catalog with fitment data — year/make/model/engine mappings, OE cross-references, compatibility rules. The configurator validates against that. No AI guessing: if the fit isn't in your data, it says so rather than guessing.
What if the customer only has a part number, not the vehicle details?+−
They can start with a part number or OE reference. Configurator cross-references to your catalog, confirms fit, and returns compatible options. If the part number is ambiguous (fits multiple vehicles), it asks for the vehicle to confirm.
Can it handle OE vs. aftermarket cross-references?+−
Yes. You define the cross-reference logic in your catalog. Customer asks for an OE part number — configurator returns your aftermarket equivalent with fit confirmed.
What about fleet orders — multiple vehicles, multiple parts?+−
Fleet manager describes the fleet (vehicle types, models, years). Configurator builds a multi-line quote: parts per vehicle, quantities, fit validated for each. One quote, not 20 separate requests.
How does it handle parts with complex fitment — engine variants, body styles?+−
The configurator asks clarifying questions when fitment depends on a variant: "Is this the 2.0 dCi or 1.6 dCi?" It narrows down to the correct part before quoting. No wrong parts shipped because of an ambiguous fitment.
Can it suggest related parts for the same job?+−
Yes. You define job-based bundles in your catalog: oil filter → oil + drain plug + gasket. Configurator suggests the full job kit. Customer gets one quote, you get a larger order.
How does the output integrate with our order management system?+−
Via API or embedded widget. Quote output (part numbers, quantities, prices) can be pushed to your OMS, ERP, or CRM. Integration options and technical specs are covered on the demo call.
Why teams trust R[AI]SING SUN
We work with regulated data: medical records, claims, customer information. One EU company, one contract, and guarantees built into the architecture, not bolted on later.
Your data
Encrypted end to end
Encrypted at rest and in transit, at all times. The same protection level banks use.
Never used for training
Your data never trains anyone's models. What you share stays exclusively yours.
Compliance
GDPR and CCPA aligned
Built for EU and US privacy law. Your data won't leak, and you won't explain anything to regulators.
HIPAA-ready, EU AI Act mapped
Access controls, encryption, and a full audit trail for regulated health and high-risk AI.
Where it runs
EU or US hosting
You pick the region. We deploy there.
On-premise option
Run it entirely on your own infrastructure. No data leaves your network.
Explore other industries
Ready to see it in action?
30-minute demo. We show the configurator in action — VIN or year/make/model, fit validated, quote ready. No pitch, no long deck.
What happens on the call
We show the configurator in action (use-case-first, one entry, validated output)
We walk through your flow and integration options (chat widget, API, or embedded)
We answer your questions and next steps if it is a fit
What you will not get
A generic demo deck or a forced enterprise upsell.