We build the full AI stack around your data: retrieval and copilots grounded in your private knowledge, agentic workflows that complete real tasks, and classical models for forecasting, scoring, and anomaly detection. Every system ships with evaluation suites, tracing, and a token and latency budget, so it holds up in front of real users instead of only in the demo.
A prototype that answers five hand-picked questions is easy. A system that answers ten thousand real ones, cites its sources, refuses when it should, and costs what you budgeted is engineering work.
We build that system: retrieval grounded in your own data, agents that call your APIs under real business rules, and classical models where a language model is the wrong tool. Evaluation, tracing, and cost controls go in from the first sprint, not after the first incident.
2 wks
Discovery to a scoped, ranked AI roadmap
6–10 wks
Prototype to a production-ready first release
Eval-first
Every model change gated by regression suites
Your cloud
Runs in your accounts, your data stays yours
We start from the workflow that costs you money or time, then choose the model class that fits, from a frontier LLM to a gradient-boosted tree.
Assistants that work over your private knowledge with permissions intact, so support, sales, and internal teams stop hunting through wikis and PDFs.
Multi-step agents that read a request, gather context, call your systems, and complete the task, with approvals wherever the stakes justify a human.
Contracts, claims, invoices, images, and call recordings turned into structured, validated records your existing systems can consume.
Forecasting, scoring, and anomaly detection where accuracy and explainability matter more than fluency, deployed behind clean APIs.
The unglamorous layer that keeps AI alive: pipelines, registries, evaluation gates, and dashboards for quality, drift, and spend.
For regulated data or high volume, self-hosted models tuned on your examples and served inside your network with no data leaving it.
A typical first engagement runs eight to twelve weeks. Each stage ends with something reviewable, so you can stop or expand on evidence.
1–2 weeks
We audit your data, workflows, and constraints, then rank candidate use cases by value, feasibility, and risk, instead of starting from whichever model is trending.
2–3 weeks
A working slice on your real data with retrieval, prompts, and a first evaluation set, so stakeholders judge quality on evidence rather than a scripted demo.
3–5 weeks
Permissions, PII handling, refusal behavior, fallbacks, and regression suites running in CI, plus load testing on the paths that actually carry traffic.
Ongoing
Staged rollout with monitoring on quality, cost, and drift, and a change process your engineers can run, including prompt updates and retraining.
Answers users trust, with citations and predictable failure behavior
Hours of manual review, triage, and data entry reclaimed
Token, GPU, and latency budgets that hold as usage grows
An in-house team confident enough to ship AI changes without us
The tools we reach for on AI & ML work, picked for the problem in front of us and for the team who inherits the code.
Where the training and fine-tuning happens
Orchestration across models and tools
Grounding answers, then serving them fast
Proof it still works after the next model swap
It depends on data sensitivity, cost at your volume, and how much control you need over behavior. We usually validate value fast on a hosted frontier model, then benchmark open-weight options once traffic patterns are clear. Mature systems often route: a small model handles easy cases, a large one handles the rest.