Software development services
AI Development
As an AI development company, we build AI that ships — not demos. From generative AI features and LLM applications to autonomous AI agents and custom machine-learning models, we build the whole system around the model: retrieval over your data, evaluation, guardrails, and the integration into your product that turns a clever prototype into something dependable.
Most AI projects die in the gap between a promising demo and a production feature. A model call is easy; the machinery around it — grounding it in your data, catching hallucinations, handling the third edge case, keeping cost and latency sane — is where real AI software development happens. That machinery is what we build.
We work across the modern AI stack: OpenAI and Anthropic models for language, retrieval-augmented generation over your own content, vector search, fine-tuning where it earns its keep, and classical ML where a large language model is the wrong tool. We're model-agnostic on purpose — the right choice depends on your accuracy, cost, latency, and data-privacy constraints, not on hype.
What we build
Generative AI features embedded in existing products — drafting, summarization, classification, and search that actually understands intent. AI agents that take multi-step actions against your tools and APIs, with the checks and human-in-the-loop controls that make them safe to trust. And AI integration work: wiring models into the software you already run, rather than bolting on a separate chatbot.
Under all of it sits the unglamorous part that makes AI dependable in production: evaluation suites so you know when a change makes things worse, prompt and retrieval pipelines you can version, fallback handling for when a model is slow or wrong, and cost controls so a feature doesn't quietly bankrupt its own business case.
What you get
Built around your data
Retrieval-augmented generation and vector search over your own content, so answers are grounded in your reality — not the open internet's.
Evaluated, not vibes
We build eval suites before we ship, so 'it feels better' becomes a number you can defend. Regressions get caught, not discovered by users.
Safe to put in front of users
Guardrails, human-in-the-loop where it matters, and graceful failure — the difference between an AI demo and an AI product.
What we deliver
- Generative AI features or LLM application
- AI agents with tool use and guardrails
- Retrieval (RAG) and vector search over your data
- Evaluation harness and quality metrics
- Model integration into your existing product
- Cost, latency, and monitoring instrumentation
How we work
- 01
Discover
We pressure-test the idea, map the users, and define the smallest thing worth building. You leave with a plan, not a proposal.
- 02
Design
Flows, prototypes, and a design system that makes the product feel real before a line of production code ships.
- 03
Build
Weekly releases in your stack. You see working software every Friday and steer with real feedback, not guesses.
- 04
Scale
We harden, instrument, and document the system — then hand off cleanly, or stay embedded. It runs without us.
Frequently asked questions
Which AI models do you build with?
We're model-agnostic — typically OpenAI and Anthropic (Claude) for language tasks, with open models where privacy or cost demands it. We pick per project based on accuracy, latency, cost, and where your data is allowed to live.
What is an AI agent, and do we need one?
An AI agent is a system that takes multi-step actions against tools and APIs, not just answers a question. It's worth it when a task genuinely requires reasoning across several steps — and overkill when a single well-designed model call would do. We'll tell you honestly which you have.
Can you add AI to our existing application?
Yes — AI integration into existing products is a large part of what we do. We wire models into the software you already run, with the retrieval, evaluation, and guardrails to make it reliable, rather than shipping a separate bolt-on chatbot.
How do you stop the AI from hallucinating?
You can't reduce it to zero, but you can engineer it down: grounding answers in your data via retrieval, constraining outputs, validating against sources, and measuring hallucination rates with an eval suite so you catch regressions before users do.
Let’s build
Have something worth building?
Tell us what you’re working on. We’ll come back within one business day with real, specific thoughts — not a sales deck.