AI + ML
Practical AI that connects to your data and processes — not a slide deck demo.
We design and ship assistants, retrieval-augmented workflows, and automations that sit inside your existing tools: support, ops, internal apps, or customer-facing chat — with logging, evaluation hooks, and human-in-the-loop where risk requires it.
When classical ML fits better than a prompt, we stay in Python land for training, validation, and deployment patterns your team can operate.
Every engagement starts with a narrow pilot: one workflow, measurable before/after, and explicit kill criteria so you are not locked into a model vendor story you cannot afford.
RAG that cites sources and fails loudly
Chunking, embeddings, and re-ranking are tuned to your corpus — not a generic “upload PDF” button. We log retrieval misses so you can improve docs instead of blaming the model.
Tool calls are schema-first: typed inputs, timeouts, and idempotency for anything that writes to your systems.
Guardrails your legal team can read
PII redaction, region constraints, and retention policies are encoded in the pipeline — not buried in a prompt footnote.
We ship dashboards for latency, cost per task, and escalation rate so finance and ops see the same truth.
Phases you can track in demos and invoices.
Use-case triage
ROI, risk class, data readiness, and human fallback design.
Baseline eval
Golden questions, rubric scoring, and competitor bench.
Pilot build
API + UI slice with logging and admin review queue.
Scale or stop
Cost model, SLOs, and either expand scope or document exit.
We meet you where your stack already lives — then standardize the pieces that reduce risk (CI, previews, observability). Below is a typical palette for this lane; exact tools are confirmed in discovery.
Momentum in adjacent launches.
What we deliver
- Discovery: use cases, data access, risk model, and success metrics
- RAG or tool-calling pipelines with eval sets and monitoring hooks
- APIs and UI surfaces wired to your auth and environments
- Documentation for prompts, data flows, and rollback procedures
What you get out of it
- Reduced manual work on high-volume, repeatable tasks
- Answers grounded in your content, not generic model drift
- A path to iterate safely as models and policies change
Not always. We start with the smallest index that answers your pilot questions and migrate when volume or latency requires it.
You do. We version prompts in repo, document change control, and hand off weights or adapters with runbooks.
Pilots include a burn estimate; production quotes add caching, batching, and model routing to keep unit economics predictable.
We default to human confirmation for high-risk outputs, cite-or-refuse patterns, and structured outputs validated against JSON schema.
Ready to scope ai + ml for your product or team?
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