Production AI, engineered end to end, six eval-gated service lines.
The same playbook, tuned to the constraints of the sectors we ship into most.
Proof, not promises, selected case studies and recognition.
A transparent, 3-phase playbook from first audit to embedded team.
The senior team behind the work, and how to reach us.
Most AI features stall in the gap between a promising demo and a system the business can rely on. We build LLM applications, autonomous agents, and RAG pipelines that clear that gap, with the eval framework and MLOps infrastructure to keep them dependable in production.
We start with the business outcome you are after, then assemble the capabilities that get you there. The same senior engineers stay with you across model selection, RAG architecture, agent design, and eval infrastructure.
Customer-facing assistants, internal copilots, and domain-specific chatbots with RAG, tool-calling, and eval frameworks baked in from day one.
Multi-step agents that plan, use tools, and complete goals without hand-holding. We architect the loop, the guardrails, and the observability.
Retrieval-augmented generation over your private knowledge vector stores, chunking strategies, hybrid search, and continuous reranking.
Domain adaptation via LoRA, QLoRA, and instruction fine-tuning. We build the training pipeline, run evals, and version every checkpoint.
OpenAI, Anthropic Claude, Google Gemini, and self-hosted Llama we handle prompt engineering, token budgeting, and multi-model routing.
Custom eval suites measuring accuracy, factuality, latency, and safety. CI-integrated so regressions block deploys, not users.
We assess your data, use-case, and latency constraints before selecting a model. No model recommendation without seeing your data first.
Before writing application code, we define the eval suite accuracy, latency, safety, hallucination rate. Every subsequent decision is measured against it.
Short build sprints with demos at each checkpoint. You see working software, not slide decks.
We hand over with CI-integrated evals, drift monitoring, a runbook, and 60 days of post-launch support.
Most scoped LLM applications ship their first production version in 6–10 weeks. The timeline depends on data readiness, integration complexity, and required eval coverage. We share a week-by-week roadmap at the end of discovery.
We run model selection on your actual data and use-case before recommending. Cost, latency, context window, and compliance requirements all factor in. We often start with a hosted frontier model and move to a fine-tuned open-source model once the eval bar is set.
Both. We regularly integrate LLM features into existing platforms Python, Node.js, Rails, or .NET. We also build greenfield AI-native apps when the brief calls for it.
It means no PII in prompt logs, encrypted-at-rest storage for embeddings, audit-logged inference calls, and a documented data-handling policy you can hand to your compliance team.
30 minutes, one of our seniors, no slide deck. By the end of the call you'll know whether we're the right team, and if not, who is.