AI ships fast and demos beautifully, but when you lean too heavily on it, unexpected problems can arise. What works in the demo can break in production, and that can cost your company real money in token bills, downtime, or abuse you did not see coming.
RAG search and semantic retrieval
We design retrieval pipelines that respect the structure of your data, with document chunking, embedding strategy, vector storage, and reranking tuned to the domain rather than lifted from a tutorial. Vector storage runs on pgvector, Pinecone, Weaviate, or Qdrant depending on scale and operational constraints. The result is sourced answers your users can verify, not hallucinated paragraphs that look right but are not.
LLM integration with proper guardrails
We integrate OpenAI, Anthropic, and open-source models with prompt engineering tied to evals, output validation, and fallback behavior when the model declines or fails. The LLM is one component in a larger system, not the system itself, so we plan for the cases where it gets things wrong.
Visual workflow automation
For teams that need to encode reusable processes, we build visual workflow builders that let non-engineers compose AI-driven pipelines with branching logic, human-in-the-loop checkpoints, and audit trails. This is the pattern we used for Crystal aOS to let legal teams configure their own compliance assessments.
LLMOps and abuse prevention
A public-facing AI feature has to assume someone will try to abuse it. We build the rate limiting, prompt injection defenses, topic guards, token usage monitoring, and cost alerting that keep an AI integration from becoming an unbounded liability. If a user tries to turn your chatbot into a free code-generation tool, you should know about it before the bill arrives.
Discovery
1 to 2 weeksWe map the problem, the users, the data, and the success metric. Most projects either need AI less than they think or need it more deeply than they think. We tell you which one applies before any code gets written.
Prototype
2 to 4 weeksWe build the smallest version that touches real data and real users, with evals from day one. If the prototype works against the metric, we know it will work in production.
Production build
VariableWe engineer the prototype into a reliable system with proper observability, fallbacks, infrastructure, and the integrations your team needs to operate it.
Iteration and handover
OngoingWe work with your team on tuning, documentation, and handover. We do not disappear when the contract ends, and we do not lock you into a relationship to keep the lights on.
We work in Python and TypeScript across OpenAI, Anthropic, and open-source models. Vector storage runs on pgvector, Pinecone, Weaviate, or Qdrant depending on scale and operational constraints. Eval harnesses are built with Promptfoo or custom infrastructure tied to the specific quality metrics that matter for the workflow.
Production monitoring covers LLM-specific signals like answer quality, retrieval recall, latency, and cost per query, so you can see when the model degrades and why. Human-in-the-loop UIs are built in React with audit trails, approval queues, and clear rollback paths.
How do you decide if a problem actually needs AI?
We start with the workflow. If the problem is fuzzy pattern matching over text or images, AI helps. If the problem is structured data with deterministic rules, classical software is faster, cheaper, and more reliable. We tell you which one applies during discovery rather than selling AI for its own sake.
What does an engagement cost?
We scope projects after discovery. A typical AI integration runs four to twelve weeks of build time after the prototype phase, billed on a fixed-scope or time-and-materials basis depending on the level of uncertainty in the problem.
Who owns the code?
You do. We deliver source code, infrastructure-as-code, and documentation. There are no proprietary frameworks you cannot maintain without us.
Which models do you use?
Whichever fits the workflow. We have shipped on OpenAI, Anthropic Claude, and several open-source models. We do not have a vendor preference and we do not let model lock-in compromise the architecture.
Can you work with our existing data team?
Yes. We integrate with your data pipelines, your authentication, and your existing applications. Our preference is to fit into what you already have rather than ask you to migrate.
What if we already have a prototype?
We can audit it, finish it, or rebuild it depending on what makes sense. We do this often enough that the audit is fast and the recommendation is honest.
Ready to implement AI?
Tell us about the workflow. We will tell you whether AI is the right tool and what it would take to ship it.