AI Integration Services That Are Tested Before They Are Trusted
AI integration means putting a language model to work inside software you already have: reading invoices into structured data, sorting incoming email, answering questions from your own documents, or adding an assistant to your product.
The difference between a demo and a feature is testing. We build a set of real examples first, measure how often the AI gets them right, and only ship when the numbers are good enough for the job, with a fallback for when they are not.
This is for you if
- ✓You want AI inside your product or internal tools, not another chat window
- ✓Staff read, sort or summarize documents by hand all day
- ✓A ChatGPT prototype was impressive in the demo and unreliable in practice
- ✓You need to know what an AI feature will cost at real volume before you ship it
This is probably not for you if
- ✕The task must be right every single time with no human check — current AI models cannot promise that
We would rather tell you now than take the project and disappoint you.
Worth knowing: Model choice matters less than people think, and it changes every few months. What lasts is the evaluation set: a few hundred real examples with known right answers. With one, switching to a cheaper or better model takes an afternoon of testing instead of a leap of faith.
What you get
An evaluation set
Real examples from your business with the correct answers, used to measure accuracy before and after every change.
Document and data extraction
Invoices, forms, contracts and emails turned into structured fields and checked against rules before they are saved.
Search over your own content
Retrieval-augmented generation (RAG) that answers from your documents and cites the passage it used.
Agents with narrow tools
Models that look things up or take actions through a small set of permitted tools, with approval steps on anything risky.
Cost and rate limits
Per-request and monthly caps, caching, and smaller models wherever they are good enough, so the bill holds no surprises.
Privacy by default
API providers that do not train on your data, sensitive fields masked before they leave your systems, and logs you control.
Where AI earns its keep, and where it does not
- Good fit — Reading and classifying text, pulling fields out of messy documents, drafting replies a person approves, and searching large amounts of your own content.
- Possible with care — Agents that take actions, such as updating records or issuing refunds, when every action is limited, logged and reversible.
- Poor fit — Arithmetic and accounting a database does exactly, decisions that need a guaranteed right answer, and anything where nobody would notice a wrong result.
What it costs to run
Most business integrations cost tens of dollars a month in model usage, not thousands. Classifying ten thousand emails with a small model typically costs a few dollars. Costs climb with long documents, large models and agents that loop, which is why we measure cost per task during testing and set hard limits before launch.
What we build it with
Portable by design, so you can move to a better or cheaper model without a rebuild.
Models
OpenAI, Anthropic, Google, and open models where they fit better
Retrieval
Postgres with pgvector, or a hosted vector database when scale needs it
Orchestration
TypeScript or Python services, queues and retries, n8n for simpler flows
Testing
Evaluation sets with known answers, run on every change
Local and private
Open models on your own hardware when data cannot leave
Hosting
Vercel, Cloudflare Workers, or your own servers
Not sure whether an AI integration is what you actually need? Send over what you are trying to fix and we will tell you straight — including if a smaller job would do it.
Ask us first →How it works
Pick one job
A single task with clear inputs and a clear right answer, chosen for value.
Build the test set
Real examples with known answers, so we can measure before we promise.
Prototype and measure
Accuracy, speed and cost per task reported in numbers, before the production build is quoted.
Ship with guardrails
Limits, logging, fallbacks and a human check where mistakes cost money.
Work you can check
Real work that uses the same skills. Every card links to something you can open and inspect yourself.
Our site assistant
Answers from a facts file so it quotes published prices instead of inventing them. The model is fixed on the server and requests are capped, because each one costs money.
AI toolingLocal AI VRAM calculator
Which graphics card holds which open model, how fast it runs, and what each token per second costs.
Questions
How much do AI integration services cost?
Focused integrations such as document extraction or email triage start at $2,500. Features inside a product with search over your data, agents or several models usually run $6,000–$20,000. Model usage is billed to your own provider account.
Which AI model do you use?
Whichever passes the evaluation set at the lowest cost. That is often a small, cheap model for classification and a larger one for drafting or reasoning. The integration stays portable, so switching later is a test run, not a rebuild.
Is our data safe with AI?
With the major API providers, data sent through the API is not used for training by default, and stricter no-retention arrangements exist for sensitive work. We also mask sensitive fields before they leave your systems, and can run open models on your own hardware when data cannot leave at all.
What is RAG, and do we need it?
Retrieval-augmented generation means the AI looks up relevant passages from your own documents before answering, and can cite them. You need it when answers must come from your content — policies, product details, contracts — rather than the model's general knowledge.
Can you add AI to our existing app?
Usually, yes. Most integrations are a server-side API call plus the plumbing around it: queues, retries, validation and logging. We work inside your codebase and follow your conventions.
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