06 / Services
Custom AI Integrations
Some work doesn't fit a template.
Custom AI integration means embedding models like Claude directly into your existing product or internal tools, with structured outputs your code can trust, retries and fallbacks for API failures, evaluation suites that measure quality, and token budgets that keep spend predictable in production.
The demo is easy; production is the hard part. We take AI from “someone on the team has a prompt” to a properly engineered feature inside your product or internal tools: structured outputs your code can trust, retries and fallbacks for when the API hiccups, evals so quality is measured instead of vibes, and token budgets so finance never gets a surprise.
In practice
What custom ai integrations actually does
The demo is easy and production is the hard part. Taking AI from someone on the team has a prompt to an engineered feature is mostly about the surrounding scaffolding rather than the model call itself.
An AI feature inside your own product
Summarization, classification, drafting or search built into your application with schema-validated outputs, so downstream code can rely on the shape of what comes back instead of parsing free text and hoping.
Internal tools with approval queues
A review interface where staff approve, edit or reject AI output before it takes effect, with every decision logged. This is what makes AI deployable in regulated and high-consequence workflows.
Cost and quality instrumentation
Per-feature token budgets, usage alerts, model tiering that routes simple work to cheaper models, and an eval suite that catches quality regressions before your users do. Finance gets a number they can forecast rather than a line item that moves with traffic.
Migration off a prototype that outgrew itself
Plenty of teams already have an AI feature working in a notebook or a single unguarded API call that quietly became load-bearing. We take that prototype and give it the surrounding engineering it never got: schemas, retries, logging, evals and a cost ceiling, without rewriting the parts that already work.
Deliverables
What you get
- Integration design: where AI adds value in your product, and where it doesn’t
- API integration with structured outputs, retries, and fallbacks
- Prompt library and eval suite so quality is measurable and repeatable
- Cost instrumentation: per-feature token budgets and usage alerts
- Engineering handover with documentation your team can build on
Outcomes
What changes
- AI features your users actually rely on not a bolted-on chat box
- Measured output quality, with regressions caught before users see them
- Predictable API spend, itemized per feature
Process
How we build it
Four stages, fixed scope, and working software in week one rather than a discovery phase that produces slides.
Week 0 to 1
Integration design
We identify where AI genuinely adds value in your product and, just as importantly, where it does not. Then we define schemas, failure behaviour and the human oversight model.
Weeks 1 to 3
Build and instrument
The integration itself with structured outputs, retries, fallbacks and bounded loops, plus logging of every prompt and response into storage you own.
Weeks 3 to 4
Evaluation suite
A set of real cases with known-good outcomes that every prompt change and model upgrade runs against, so quality is measured rather than assumed.
Weeks 4 to 6
Cost controls and handover
Model tiering, prompt caching, hard budgets, then engineering handover with documentation your team can build on.
Integrations
What it integrates with
If a system has an API we can connect to it. These are the tools we wire into most often, and the list is not exhaustive.
- Claude API
- OpenAI API
- TypeScript
- Python
- Postgres
- Vercel
- AWS
- Supabase
- Stripe
- Slack
- HubSpot
- Salesforce
- Notion
- Airtable
- n8n
Investment
What it costs
Custom integrations are scoped per engagement because they touch your codebase. Smaller feature integrations typically start around $1,997 setup, and larger production programmes with eval suites and cost instrumentation are quoted after the audit call. Every quote is fixed before work begins.
See full pricing for every tier or estimate your return first.
How long it takes
| When | Focus | What you get |
|---|---|---|
| Week 0 | Technical audit of your stack | Integration design and fixed price |
| Weeks 1 to 2 | Core integration build | Working feature behind a flag |
| Weeks 3 to 4 | Evals and instrumentation | Measured quality and cost |
| Weeks 5 to 6 | Hardening and handover | Production release and docs |
Evidence
Where this has been built
Each build is labelled honestly as an internal build, a solution blueprint or a productized template, and documented end to end.
Related reading: Deploying Custom AI Agents for Enterprise-Scale Work
FAQ
Frequently asked questions
The questions buyers ask about custom ai integrations before booking a call.
Typical stack
Ready to put your business on autopilot?
One call. We map the hours you're losing and quote the system that gets them back scoped and priced before you commit to anything.