A working demo is not a production system.
Most AI looks great in a meeting and quietly breaks in the real world. We engineer every system we ship for data control, compliance, accuracy and safety by default, then we run it so it stays that way.
The five things that decide it
Production AI demands more than a model that answers.
These are the five things we get right before anything goes live, and keep right every month after.
Your data, your control
We do not train foundation models on your data. We work with providers under contracts that forbid training on your inputs and outputs, and we mask PII and sensitive fields before they ever reach a third-party model.
Compliance by design
SOC 2 controls, ISO 27001 alignment and HIPAA-ready handling for healthcare work. GDPR by default, with region-specific data residency across the US, EU and India where you need it.
Evaluation & observability
Every model we put live is wrapped in an evaluation suite scoring accuracy, hallucination rate, bias, latency and cost, with continuous monitoring. We re-test after every provider model upgrade.
A human in the loop
For decisions that move money, hire people or touch clinical and legal outcomes, a qualified human reviews and approves. Automation handles the volume; a person owns the risky edge.
We run it, not hand it over
After launch we monitor, patch and improve the system, and report on it every month. Responsible AI is not a one-time audit, it is an operating discipline we own with you.
Built on trusted models
We build on OpenAI, Anthropic, AWS Bedrock and self-hosted open-weight models, choosing the right one for your accuracy, cost, privacy and residency needs, not a single vendor lock-in.
Evaluation, not vibes
We score every system against the numbers that matter before you trust it.
A demo proves the happy path. We prove the rest: how often it is right, how often it makes something up, how it handles the edge cases, and what it costs to run at your volume. The scorecard is shared, not hidden.
- Accuracy & hallucination rateMeasured on a held-out set built from your real cases, not generic benchmarks.
- Bias & safety checksRed-teamed before launch and re-checked after every meaningful model change.
- Latency & cost per taskSo the economics are clear before you scale, not a surprise on the invoice.
How we ship it
Responsible by default, in five steps.
Scope & risk map
We classify what the system can read, write and decide, and flag every high-stakes action up front.
Govern the data
Least-privilege access, PII masking and a no-training contract with the model provider before a line ships.
Evaluate & red-team
Accuracy, hallucination, bias and cost scored on your real cases. We try to break it before users can.
Launch with guardrails
Confidence thresholds, human approval on the risky 20%, and a rollback plan that works instantly.
of enterprise AI pilots never reach dependable production. The gap is almost never the model, it is the governance, evaluation and operations around it.
- A shared evaluation scorecardUpdated every month, not a one-off slide.
- An audit trail of every AI actionLogged from day one for compliance and trust.
- A named team that owns uptimeNot a ticket queue, the people who built it.
Questions teams ask us
Responsible AI, answered plainly.
Do you train AI models on our data?
What compliance frameworks do you support?
How do you stop the AI from making things up?
Who is accountable after launch?
Which models do you build on?
Want AI you can actually put in front of customers? Let's talk.
Bring us the workflow you want to automate. We will tell you, honestly, what is safe to ship, how we would evaluate it and what it costs to run.