For teams turning data into product features and automation.
Ship AI features that help users make better decisions.
We focus on measurable impact: accuracy, latency, cost, and safety constraints.
Problems we solve
Models look good offline but fail in production
Cost per request or GPU time is unpredictable
Safety, PII, and audit requirements are unclear
No feedback loop from user behavior to retraining
What you get
Problem framing: metric, baseline, and guardrails
Data pipeline sketch + labeling strategy if needed
Serving path with latency and fallback behavior
Evaluation harness and monitoring for drift
Typical timeline
Weeks 1–2: feasibility, dataset audit, risk list
Weeks 3–6: baseline model + offline metrics
Weeks 7–10: production integration + human review hooks
Ongoing: cost tuning and periodic re-evaluation
How we work
Human-in-the-loop where stakes are high
Documented prompts, versions, and rollback for LLM features
Privacy review before training on customer data
Clear owner for model cards and incident response
Tech we commonly use
Python
PyTorch or cloud ML APIs
Vector DBs when needed
FastAPI / Node gateways
OpenAI / Anthropic APIs
GPU cloud
FAQs
Do you fine-tune or use APIs?
We pick the smallest thing that works—often retrieval + API models first, fine-tuning only when metrics justify it.
How do you reduce hallucinations?
Grounding, citations, confidence thresholds, and safe fallbacks—plus UX that sets expectations.
Can you run on-prem or VPC-only?
Yes, with air-gapped patterns and private endpoints where your policy requires it.
What’s included for compliance?
Data handling docs, access logs, model versioning, and red-team style checks for high-risk flows.
Ready to move?
Share your goal and constraints. We’ll suggest the smallest practical next step.