AI integration

LLM features added to the product you already have. Grounded in your data, engineered for cost, shipped to production.

What we add

Search that understands meaning

Semantic search over your products, documents, or knowledge base. People find what they meant, even when they typed something else.

Chat grounded in your data

Assistants that answer from your documentation and records using retrieval, and say "I don't know" instead of guessing.

Document processing

Invoices, contracts, forms, and reports read automatically. Structured data out, verified against the source.

Workflow automation

The messy human steps in your process, like categorizing tickets or drafting replies, handled by a model with review where it matters.

Classification and routing

Emails, leads, tickets, and content sorted and routed by meaning, at a fraction of the cost of doing it by hand.

Content generation

Product descriptions, summaries, and reports drafted in your voice, from your data, with humans approving the output.

The machinery behind a good integration

The model call is a small part of the work. These are the parts that make an AI feature dependable.

Retrieval pipelines with embeddings and vector search, so answers come from your data
Automated evals against known-correct answers, run before every release
Prompt caching and model routing, so routine calls run on cheaper models
Guardrails and graceful fallbacks when the model is uncertain
Cost and quality monitoring in production, per feature
Structured outputs, so your code can use model responses directly

How it runs.

Audit

We look at your product and find where AI pays off. Sometimes the honest answer is that it doesn't, and we tell you.

Prototype

A working slice inside your real product with your real data, usually within the first weeks. You judge it by using it.

Ship

Quality evals, cost controls, monitoring, and fallbacks. An AI feature is production software and gets treated like it.

Questions clients ask

Short answers, no sales language. If yours isn't here, ask an engineer directly.

Ask us anything

How do you stop it from making things up?

We ground answers in retrieved data instead of the model's memory, run automated evals against known-correct answers before every release, and design the feature to show when the model is unsure.

Can you work with our existing stack?

Almost certainly. We integrate AI into .NET, Next.js, React, and Node codebases routinely, and the model providers are just HTTP APIs underneath.

What will it cost to run each month?

It depends on volume and design, so we estimate it per feature before building. Prompt caching, smaller models for routine steps, and batching often cut running costs several-fold compared to a naive build.

How long does an integration take?

A first working prototype typically lands within a few weeks. Production hardening depends on the risk level of the feature: a content drafting tool ships faster than automation that touches customer money.

Can you add AI to our existing CRM or ERP?

Usually, yes, if the system has an API or a database we can reach safely. Common additions are search over your records, drafted replies, and sorting incoming emails or tickets. We first check what your system exposes and where AI would save real time. If a plain rule or report does the job, we say so. We bill $30 an hour, or by milestone from a written scope.

AI models and ChatGPT: all posts AI in your product: all posts MCP servers: all posts AI agents: all posts

Have a feature in mind?

Describe your product and the problem. We reply with an honest take on whether AI is the right tool.

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