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 is the small part. What makes an AI feature dependable is everything around it.
- 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 model responses plug into your code, not just chat
How it runs.
Audit
We look at your product and find where AI genuinely 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 we actually get
Short answers, no sales language. If yours isn't here, ask an engineer directly.
Ask us anythingHow 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 so an uncertain answer degrades gracefully instead of bluffing. Hallucination is an engineering problem, and engineering problems have fixes.
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. We adapt to your codebase, not the other way around.
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.
Have a feature in mind?
Describe your product and the problem. We reply with an honest take on whether AI is the right tool.