Custom AI That Ships
Not Another Pilot

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.

3–5 wks
Production path
1–2 wks
Prototype in sandbox
$18,400+
Complete solution from
Fixed
Scope, price, stages

Which Path Fits

Before scoping custom work, we check whether a catalogue product or a consulting engagement gets you there faster and cheaper.

// 01

Ready-to-Run

Repeatable bottleneck with a known solution pattern. Faster to deploy, lower cost when a product matches your use case exactly.

View all products →
// 02

Strategy First

Need an audit, architecture design, or compliance framework before committing build budget. May conclude “don’t build — buy X instead.”

View all services →
// 03 — You're here

Your Stack, Your Rules

Unique workflow, proprietary data, or deep integrations no catalogue product covers. We write the code, deploy to your infrastructure, and own observability until handoff.

  • Frontend, backend, AI core, deploy
  • KPIs before code — prototype first
  • Fixed scope, fixed price, staged payments
  • IP buyout available
See our process →

Honest check: if Booking Agent, Talkulate AI CPQ, or another catalogue entry fits your use case, we say so before signing a custom contract.

How We Deliver

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.

Optional
Optional

AI Consulting

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.

  • Process audit: where AI creates genuine leverage vs. where it adds complexity
  • Options analysis: SaaS product, automation (n8n / Make), or custom build
  • Risk and cost estimate for each path — before any code commitment
  • Output: decision memo with a clear recommendation
See AI Consulting →
2–5 days

Scope & KPIs

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.

  • Define the single metric that proves it works
  • Map triggers, edge cases, and human handoff rules
  • Agree stop criteria — what "fail" looks like at the prototype stage
  • Output: scope doc + acceptance checklist, signed off before build starts
Paid Milestone
3–7 days

Data & Integration Audit

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.

  • Inventory all data sources and access rights
  • Assess data quality and normalisation requirements
  • Choose architecture: RAG vs DB validation vs MCP vs hybrid
  • Identify integration constraints before building against them
Paid Milestone
1–2 weeks

Prototype

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.

  • Working system on real or production-equivalent data
  • First measurement against agreed KPIs
  • Decision gate: proceed to production build, adjust scope, or stop
Paid Milestone
2–4 weeks

Production Build

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.

  • Full auth, rate limiting, and security hardening
  • Monitoring (Langfuse or equivalent) and structured alerting
  • Fallback logic and human escalation paths for every known failure mode
  • Error handling and graceful degradation throughout
Paid Milestone
3–5 days

Deploy & Handoff

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.

  • Production deployment with CI/CD pipeline
  • Runbook: incident procedures and escalation paths
  • Team training on monitoring and basic operations
  • Full IP transfer per agreed contract terms
Stop Rule
Per KPIs

Iterate or Stop

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.

You Provide
  • Access to data sources and APIs (read-only is enough to start)
  • One decision-maker — 1–2 hours per week for check-ins
  • Test cases and acceptance scenarios for each milestone
We Deliver
  • Repository with full documentation and test coverage
  • Deployment with monitoring, alerting, and fallback logic
  • Integration runbook and incident escalation procedures

What We Build

Six types of custom AI work — each with a defined use case, typical timeline, and clear boundaries of what fits and what doesn’t.

01

Document Processing

Messy input in. Structured data out.

Typical timeline2–4 weeks

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.

Good Fit
  • Multiple document formats from different suppliers or labs
  • High-volume extraction needing DB insert or downstream automation
  • Regulated domains (healthcare, legal, finance) requiring audit trail
  • Existing manual extraction costing measurable staff hours per week
Not a Fit
  • Single-format clean digital exports — parse them directly
  • One-time extraction job — a script is faster and cheaper
02

Privacy-Safe AI

Automate without leaking PII.

Typical timeline2–3 weeks

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.

Good Fit
  • Healthcare, legal, or finance with patient or client documents
  • GDPR, HIPAA, or EU AI Act requirements in scope
  • Audit trail required for investor DD or enterprise procurement
  • Automation blocked by legal or security sign-off on data handling
Not a Fit
  • Public-domain content with no personal data — skip the compliance layer
03

Operational Agents

One bottleneck. One agent.

Typical timeline3–5 weeks

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.

Good Fit
  • DM-to-booking or lead qualification requiring 24/7 coverage
  • Internal request routing where human triage is the bottleneck
  • Platforms with accessible API (Instagram, WhatsApp, CRM, calendar)
  • Process too specific for a catalogue product but not structurally unique
Not a Fit
  • Use case covered exactly by Booking Agent or Career Finder — the product is faster and cheaper
  • Platform with no usable API access
04

Research Agents

One question. All sources. Verified output.

Typical timeline2–4 weeks

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.

Good Fit
  • Tender monitoring and analysis across multiple procurement platforms
  • Prospect identification and enrichment: structured brief before every sales call or proposal
  • Competitive intelligence: tracking competitors, pricing, and product changes across sources
  • Supplier or partner screening across registries, public records, and news
  • Regulatory and market monitoring across jurisdictions or industry sources
Not a Fit
  • Single-source lookup or a standard API query — no agent layer needed
05

AI Ecommerce

Your store. Agent-readable. AI-shoppable.

Typical timeline3–6 weeks

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.

Good Fit
  • Retailers with 500+ SKUs lacking structured data for AI agent visibility (AO)
  • B2B merchants with contract pricing, MOQs, and approval rules invisible to standard product graphs
  • Non-Shopify platforms needing ACP/WebMCP checkout integration for agent-initiated purchases
  • Companies building conversational shopping assistants or procurement agents for their buyers
Not a Fit
  • Shopify stores under 100 SKUs — the platform handles agent checkout automatically
06

Life Sciences AI

Domain knowledge built in. Not approximated.

Typical timeline3–6 weeks
Why us

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.

Good Fit
  • Biotech and pharma teams automating literature review, patent analysis, or target identification
  • Diagnostics labs with multi-format data pipelines requiring structured output and audit trail
  • Clinical research with data quality, traceability, and regulatory submission requirements
  • Hospital and clinic operators automating documentation, coding, or patient flow
  • MedTech and CRO companies drafting CAPA, deviation reports, or CTD sections
Not a Fit
  • Genomics model training from scratch — bring in a specialist ML research lab

What You Can Order

Two scope levels, depending on what your team can own after handoff.

// Scope A

AI Core Only

Your team owns UI, deployment, and infrastructure

  • Agents, prompts, and tool calling logic
  • API integrations and data pipelines
  • Tests and evaluation harness
  • Monitoring instrumentation (Langfuse-compatible)
  • Handoff documentation for your engineering team
Entry point

Below $18,400 depending on scope

// Scope B

Complete Solution

We own the full stack — frontend to production deployment

  • Full UI, API layer, AI core, and integrations
  • Production deployment on your infrastructure
  • Monitoring, alerting, and fallback logic
  • Runbook and incident escalation procedures
  • Compliance documentation for regulated domains
From

$18,400+

Commercial Model

No T&M Ever

we hate it, you hate it, so let's forget it

Fixed-Stage Payments

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.

IP Rights

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.

Post-Launch Support

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.

What We Decline

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.

Shipped Custom Work

Different industries. Same formula: production system, measurable outcome, real integrations.

HealthTech

AI Healthcare Startup

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.

  • Data recognition: 82% to 98% stability, ~35% faster processing
  • Clinical AI interpretation: 99% accuracy with full audit trail
  • GDPR, HIPAA, and EU AI Act compliance built in from day one
  • Pilot contracts signed with multiple clinics post-launch
  • Seven-figure investment round closed

From prototype to seven-figure investment round in one month — 98% recognition stability, 99% clinical accuracy

Enterprise Tech

US Server Reseller

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.

  • Quote cycle time: 1–2 days → 15 minutes for standard configurations
  • First-pass configuration accuracy: 76% → 100% — engineer review step eliminated
  • Presales engineering time freed: ~22 hours/week across 3 engineers
  • Quote volume capacity: +340% without adding headcount
  • 5 weeks from kick-off to production — PostgreSQL, Salesforce, Slack, Email

From 2-day quote cycles to 15 minutes — presales engineering bottleneck eliminated with AI guided selling

Marketing Agency

Beautyvers

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.

  • AI response time: under 2 minutes, 24/7 including weekends and holidays
  • 286% average booking conversion growth across salon clients
  • Stable, predictable calendar load — no more volatile weeks
  • Full CRM and Google Sheets integration — booking actually completed
  • Higher ROI on marketing spend for the agency's clients

286% booking conversion growth, 15 hours/week saved, stable client load — an AI assistant 24/7 that converts DMs into revenue

AI Sandbox

AI Battle - Agent vs Agent

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.

  • Schema Guided Reasoning — every decision traced step by step, not black-boxed
  • Full session replay — read your agent's reasoning after every match
  • Domain-specific tools per game — isolatable, testable, replaceable
  • Chess, poker, and pluggable game formats
  • Debugging in minutes — model reasoning, tool output, or their interaction

Build your AI agent. Watch it reason. Understand why it lost.

All cases →

FAQ

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.

Ready to Scope a Build?

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.

Custom AI Development — Production-Ready in 3–5 Weeks