AI built for your specific workflow, data structure, and integrations — when no catalogue product fits. Frontend, backend, AI core, deployment. KPIs agreed before code; if the prototype misses them, we stop — not scale something broken.
Before scoping custom work, we check whether a catalogue product or a consulting engagement gets you there faster and cheaper.
Repeatable bottleneck with a known solution pattern. Faster to deploy, lower cost when a product matches your use case exactly.
View all products →Need an audit, architecture design, or compliance framework before committing build budget. May conclude “don’t build — buy X instead.”
View all services →Unique workflow, proprietary data, or deep integrations no catalogue product covers. We write the code, deploy to your infrastructure, and own observability until handoff.
Honest check: if Booking Agent, Talkulate AI CPQ, or another catalogue entry fits your use case, we say so before signing a custom contract.
Most AI projects fail after handoff: no owner, metrics defined too late, pilots without fallback. We fix KPIs, scope, and stop criteria before implementation begins.
Not sure whether custom development is the right path? Before scoping a build, we run a consulting engagement: audit your processes, identify where AI creates real leverage, and map the options — catalogue product, automation, or custom. You get a decision, not a roadmap to sell you more work.
One process. One measurable target: conversion rate, SLA, cycle time, share of manual work removed. We define triggers, constraints, and human-in-the-loop checkpoints in a 1–2 page scope doc. No code until the target is written and agreed.
Every source that feeds the system: APIs, databases, file formats, permissions, data quality. We decide architecture here — RAG, direct DB validation, MCP bridge, or hybrid — based on accuracy requirements, not popularity.
Running in a sandbox on your real data — not a slideshow. Paid as a separate milestone so you see working output before committing the full build budget. Prototype metrics are measured against the KPIs from step 00.
Auth, rate limits, monitoring, structured alerts, fallback paths, human escalation for edge cases. Built for failure from day one — not because we expect it, but because production always hits what demos never show.
Infrastructure deployment, runbook documentation, team onboarding. IP rights are defined in the contract before work starts — full buyout or non-exclusive license, your choice, not a post-delivery negotiation.
If the prototype misses KPIs — we stop. No sunk-cost rollout to "give it more time." We reassess: different scope, different architecture, or a different solution entirely. If KPIs are hit — next scope is negotiated as a fixed stage.
Six types of custom AI work — each with a defined use case, typical timeline, and clear boundaries of what fits and what doesn’t.
Messy input in. Structured data out.
PDFs, scans, photos, lab reports — AI that reads raw documents from multiple sources with varying formats, normalises structure, validates output, and routes low-confidence cases to human review. Handles the format variation that breaks off-the-shelf OCR.
Automate without leaking PII.
GDPR and HIPAA compliance built into the architecture from day one — not added later. Automated PII detection and removal, structured access logging, data minimisation before the model sees the document. Compliance becomes a procurement advantage, not an obstacle.
One bottleneck. One agent.
Agents that handle a single repeatable operation end-to-end: respond to DMs, qualify leads, book appointments, route internal requests — without human triage. Clear fallback to a human when the agent hits an edge case it should not handle alone.
One question. All sources. Verified output.
Agents that autonomously gather, aggregate, and structure information from multiple sources — web, public registries, tenders, databases, CRM, internal documents — and return a structured, auditable brief. The use case is any repetitive research task: monitoring tenders across procurement platforms, identifying and qualifying prospects, mapping competitors, vetting suppliers, or tracking regulatory changes. Human review is triggered when confidence drops below threshold or a critical flag is raised.
Your store. Agent-readable. AI-shoppable.
Ecommerce infrastructure for the agentic commerce era. We build catalog enrichment pipelines that make products visible to AI shopping agents, ACP/WebMCP checkout integration for non-Shopify platforms, B2B procurement agents, and conversational shopping assistants. As AI agents become a dominant commerce channel, structured catalog truth and agent-compatible checkout become table stakes — not a future consideration.
Domain knowledge built in. Not approximated.
Co-founder holds an advanced degree in life sciences, with research background in molecular biology and diagnostics — domain knowledge is built into the architecture, not approximated from documentation.
AI systems for regulated science: clinical data pipelines, literature mining agents, regulatory document drafting, lab workflow automation, and diagnostic intelligence. Compliant with GxP, GDPR, and EU AI Act requirements by design.
Two scope levels, depending on what your team can own after handoff.
Your team owns UI, deployment, and infrastructure
Below $18,400 depending on scope
We own the full stack — frontend to production deployment
$18,400+
No T&M Ever
we hate it, you hate it, so let's forget it
The prototype is a separate paid milestone. Each subsequent phase has defined deliverables, timeline, and invoice. No open-ended billing that settles at the end of a long engagement.
Full buyout — you own the code entirely. Or non-exclusive license — we retain architecture pattern reuse rights. Your choice, written into the contract before work starts. Price is the same either way.
Monitoring, incident response, and improvements are a separate retainer — not bundled indefinitely into the build price. You pay for what you actually need after launch.
We turn down projects we cannot do well. Here is what that looks like.
"Give us ChatGPT for the whole company"
Without a defined process and measurable outcome, this fails — expensively.
Free pre-sales architecture
A scoping call and short proposal are free. But system design, data flow diagrams, and effort estimates are paid work — that's Phase 0. We don't spend weeks on architecture under promise of a contract later.
Equity or deferred payment deals
Build now, get paid if it works — we don't take risk on your business model. We work against agreed milestones at market rates. We build only what we control end-to-end — scope, stack, and delivery.
Custom ML model training
Designing and training proprietary models from scratch is data science, not AI integration. We build on top of foundation models. For custom training pipelines we bring in specialist partners — ask us to connect you.
Before committing to a build, consider:
Different industries. Same formula: production system, measurable outcome, real integrations.
One month. A prototype that crashed on real data. A healthtech startup that needed a production-ready blood test interpretation AI before their Series A pitch — and had to show it was GDPR and HIPAA compliant. We delivered it. They raised seven figures.
From prototype to seven-figure investment round in one month — 98% recognition stability, 99% clinical accuracy
Every quote took 1–2 days — account managers couldn't configure a 3,400-SKU catalog without an engineer. They tried RAG. Engineers still checked every output. We replaced it with a dual-agent system that validates against the actual database. 15 minutes. Zero engineer review.
From 2-day quote cycles to 15 minutes — presales engineering bottleneck eliminated with AI guided selling
A beauty salon owner told us: "I lose clients every weekend because I can't reply fast enough on Instagram." The math was brutal. We fixed it with an AI booking assistant that never sleeps — and the booking conversion numbers still surprise us.
286% booking conversion growth, 15 hours/week saved, stable client load — an AI assistant 24/7 that converts DMs into revenue
Chess players and poker enthusiasts spend years developing strategy. AI builders spend hours tuning agent logic. But there's never been a proper sandbox where you could pit your strategy against another, watch the reasoning happen, and actually understand why your agent lost. We built it.
Build your AI agent. Watch it reason. Understand why it lost.
Consulting is strategy and architecture — it may conclude “don’t build, buy X instead” or “fix your process first.” Custom development is execution: we write code, deploy, and ship. We keep them separate so consulting stays objective — we won’t recommend building just to sell you development. If you’re unsure which you need, start with a consulting engagement first.
If a catalogue product covers your use case — we’ll say so before scoping custom work. Booking Agent, Talkulate AI CPQ, and Career Finder are faster to deploy and cost less when the workflow matches. Custom makes sense when your data structure, integration requirements, or process logic is specific enough that adapting a product is harder than building right from the start.
The prototype is a separate paid milestone — typically 1–2 weeks, explicitly priced before it starts. You pay for the prototype, see working software on your real data, and only then decide whether to proceed to the full build. This is the only point where the decision is yours before committing to the larger investment. Rare exception: a tight scope with a previously proven stack where prototype and production are practically the same deliverable.
Your choice, agreed in the contract before work starts: full buyout (you own everything, we retain nothing) or non-exclusive license (you use it; we retain the right to reuse architecture patterns in other projects). Both options are available at the same price — this is a legal, not a commercial, distinction.
Yes. We can own the AI layer while your team owns infrastructure, or pair on architecture decisions and code review. Works best with a clear ownership boundary: we own X, you own Y, weekly sync. We don’t disappear into a silo — progress is visible at every check-in.
Yes — and we assess exactly this in Phase 01 (Data & Integration Audit) before any code is written. Common systems we work with: SAP, Salesforce, HubSpot, Microsoft Dynamics, NetSuite, custom databases, and internal APIs. Read-only access is enough to start. We map data sources, access rights, and data quality in the audit phase so there are no integration surprises during the build.
We stop. No sunk-cost rollout to “give it more time.” We reassess: different scope, different architecture, or sometimes a different solution entirely. You’ve spent prototype budget — days, not months — and you have real data about what the system can and cannot do. That is more valuable than discovering the same thing six weeks into production.
No 30-page proposals. We look at your bottleneck and tell you honestly whether custom development is the right call — or whether something else gets you there faster.
Prefer email? [email protected]
Tell us: 1) the bottleneck, 2) what you’ve tried, 3) rough timeline and budget.