Service 01
Agentic AI in Production
Most AI agents break in production. They hallucinate, loop, or fail silently. We build agentic systems with the guardrails, fallbacks, and observability to stay working under real load.
Services
Audit. Build. Keep shipping.
From the audit that finds your first use case to the evals that keep production honest. Seven services, one engineering partner.
Service 02
AI Readiness Audit & Roadmap
No frameworks, no maturity scorecards, no four-quadrant matrices. We interview your operators, audit your data, and hand back a short list of AI bets ranked by ROI and effort, with the first one already scoped enough to start building. A board-defensible answer to "what's our AI strategy", delivered in weeks instead of quarters.
Service 03
RAG & Memory Architecture
Generic AI gives generic answers. We build retrieval and memory systems grounded in your proprietary data: your documents, your policies, your customers. AI answers from your business, not the internet.
Service 04
AI Reliability: Evals, Governance, Cost
Most AI pilots die between demo and production. We build the reliability layer that keeps them shipping: evals to catch hallucinations and regressions, governance for policy and audit trails, cost optimization to keep LLM bills from doubling every quarter.
Service 05
Voice AI
Voice AI that doesn't drop calls or get stuck in loops. We build front-desk agents that handle appointment scheduling, rescheduling, and customer support around the clock, plus domain-tuned transcription for clinical and back-office workflows.
Service 06
AI Product Engineering
Most AI features ship as a side project bolted onto a real product. We build the AI product as a real product (auth, infra, evals, on-call runbook), so it gets the same engineering rigor as the rest of your stack. The on-call engineer at 3am can read the runbook. The compliance reviewer can read the audit logs. The user doesn't notice anything weird.
Service 07
Fractional AI Leadership
Most AI initiatives stall not because the engineering's wrong, but because no one senior is making the calls: what to fund, what to kill, what to defer. We step in as the fractional AI voice in the room: strategy with leadership, oversight with engineering, plain-English readouts for the board. Direction first, delivery second.
FAQ
Frequently asked questions
What's the difference between an audit, a quick-win build, and production work?
The audit is a 1 to 2 week diagnostic that ranks AI bets by ROI and effort. A quick-win build is a 4 to 6 week scoped V1 shipped to production for real users. Production work is the ongoing reliability layer: evals, governance, cost controls, and tuning for actual load. Most clients enter at the phase that matches where their problem already is.
Do you build AI agents, or only advise?
Both, with no separation between the team that advises and the team that builds. Code lives in your repos. We design the system, write production code, set up evals, and stay through the reliability work. Strategy without delivery is what most consultancies do. We do not stop at the deck.
How do you decide whether AI is the right tool for a problem?
Problem first, tool second. We look at the workflow, the failure cost, and the data quality before we look at the model. Sometimes a simple heuristic, a SQL query, or a forms cleanup beats an LLM. If AI is not the right tool, we say so on the audit call rather than billing for a system that should not exist.
How do you keep AI systems reliable after launch?
Production reliability is its own phase, not an afterthought. Evals catch regressions before users do. Governance and audit trails cover policy and compliance. Cost controls keep LLM spend from doubling every quarter. Reliability tuning targets your actual load, not demo load. The same engineers who built the system stay through the on-call runbook.
Can we engage you for one phase, or do we have to commit to all three?
Pick the phase you are in. The audit, quick-win, and production phases are each scoped on their own. Many teams come in mid-stream: they already know what to build and need a team that can ship, or they already shipped and need the reliability layer. There is no all-or-nothing commitment.
What size company do you work with?
From early-stage startups deciding their first AI bet to enterprises operating systems at scale. The unit of work is engineering judgment, not company size. What matters is that the team funding the work cares about outcomes that show up in production, not maturity scorecards or framework adoption.
Let's talk
Let's build something that actually works.
Tell us where you are and what you need. We'll come back with a clear, honest plan within 48 hours.