AI Engineering Services

How This Works

Most teams don't arrive asking for a service. They arrive with a symptom: an inference bill climbing faster than usage, a model that passes the demo and then regresses in production, a workload that won't run on the hardware it has to ship on, or an AI feature nobody will sign off because the evidence isn't there.

Our job is to map that symptom to one of four directions production AI work moves in, then to the one pack that closes it. Every pack ends the same way: with something your team keeps and can re-run after we leave.

Step Two: The Five Packs

• Inference Cost-Cut Pack: Cost. Measured before/after report on a named workload, plus the implemented changes. (4–8 weeks)
• Production AI Monitoring Harness: Reliability. Eval harness, regression suite, and release-gate checklist on a named system. (4–10 weeks)
• AI Porting & Deployment Pack: Portability. Working workload, benchmark report, and deployment runbook on a named target. (2–4 wk feasibility, then 4–10 wk porting)
• LLM Selection Pack: Trust. Eval suite, risk-and-comparison report, and a re-run script on named LLM candidates. (3–6 weeks)
• AI Readiness Scorecard: Trust. Filled scorecard against a named rubric, evidence map, and remediation backlog. (2–5 weeks)

Where the Packs Meet

Three kinds of work sit across pillars on purpose. They show up on both sides, and which pack owns them depends on the question you're asking, not the underlying engineering:

• Porting for performance. Start with the AI Porting & Deployment Pack when the AI path doesn't run on the target yet. Start with the Inference Cost-Cut Pack when the target is mature and the question is cost, not initial porting.
• Shelf and store analytics. Start with the Inference Cost-Cut Pack when the question is cost-per-throughput at retail scale. Start with the Production AI Monitoring Harness when the question is alert quality or false-positive economics.
• MLOps hardening. Start with the Production AI Monitoring Harness for the harness itself. Start with the AI Readiness Scorecard when the question is scoring the programme against ML Test Score or NIST AI RMF.

Not Sure Which Pack?

Match the symptom you can name to the pack that closes it:

• “Our inference bill is growing faster than our usage.” → Inference Cost-Cut Pack
• “It passes our demo but regresses in production.” → Production AI Monitoring Harness
• “We need this on hardware, a browser, or silicon it doesn't run on yet.” → AI Porting & Deployment Pack
• “Procurement needs evidence to approve an LLM for our task.” → LLM Selection Pack
• “Score us against NIST AI RMF, ML Test Score, or a readiness rubric.” → AI Readiness Scorecard
• “Certify us.” → Out of scope, we'll refer you to legal and regulatory partners.

By Industry

The pack catalogue is industry-agnostic by design. If you know your vertical, the industry pages pre-filter to the work that matters there and route each piece to its owning pack:

• AI-infrastructure / SaaS: inference cost, MLOps hardening, porting, LLM evals.
• Life sciences: medical-imaging validation, HIPAA / GxP boundary work. No clinical-decision claims; no regulatory sign-off.
• Manufacturing & automotive: industrial CV inspection, automotive perception for civilian vehicles. No autonomous-targeting work.
• Media & telecom: video pipeline cost-cut, content moderation, operational anomaly detection on your own infrastructure.
• Retail: shelf-execution validation, visual-search cost-cut. Scoped to stock and catalogue, not shopper tracking.

Start a Conversation

If one of the five packs matches the question you're trying to close, the named pack page above is the right entry point. If you're not sure, tell us the symptom and we'll route you to the pack, or tell you honestly if the work falls outside what we take on. Where we draw that line, and why, is published on our values page.

Start a conversation Tell us the problem