Senior-led advisory that pinpoints high-value AI use cases, de-risks deployment, and turns them into measurable business outcomes — grounded in the agentic platforms we build and run every day. Fixed-scope engagements, decision-grade artifacts, and an exit ramp at every gate.
Four patterns we see across organizations under pressure to "do AI" — each an argument for starting with advisory rather than procurement.
The demo wowed the review committee. Two years on it still serves one team in one location — integration, governance, and unit economics were never scoped.
Staff paste sensitive material into public chatbots to get work done. Proprietary data is leaving the building ungoverned, and every paste is a question someone will eventually have to answer.
Boards, auditors, and regulators are asking where AI touches consequential decisions — questions with no clear owner and no evidence trail behind them.
Decades of systems grew apart. Use cases get funded before anyone checks whether the data underneath can actually carry them.
A fixed-scope, contractual service that moves an organization from ideation to production AI — assessing readiness, building the roadmap, standing up governance and security, and staying through pilot, production, and scale.
Each advisory vertical is anchored by a production platform we built and run — so priorities, pilots, and estimates come from evidence rather than conjecture.
Sourcing and procurement automation — from supplier discovery and vetting through negotiation and stewardship.
Clinical care delivery and payer claims operations, closed-loop from encounter to payment.
Narrative-first investigations with entity graphs and audit-grade chain of custody.
Institutional memory turned into instant, actionable intelligence for every team that acts on it.
Two verticals have a dedicated advisory edition with industry-specific readiness scoring, value maps, and compliance framing:
Every engagement runs on the same frame — so nothing important is skipped, and nothing fashionable is smuggled in.
Every use case tied to a P&L or mission line, with a named owner and a measurable outcome. No use case advances on novelty alone.
Data, platform, talent, and process scored honestly against benchmark — before a dollar of build is committed.
Responsible, auditable, explainable AI engineered in from the first workshop, never bolted on before launch.
The people who will use the system are trained and enabled — so AI sticks in the workflow, not just in the boardroom.
Four fixed-scope engagements, each separately contracted with named deliverables. You re-decide at every gate — momentum is earned, never assumed.
Not observations — instruments. Each artifact is built to move a specific decision: fund, fix, pilot, or stop.
Where you stand across six dimensions, scored against benchmark, with every gap mapped to a costed remediation.
Three to five priority use cases tied to real ROI, with named owners, source systems, and deployment order.
A sequenced 90-day plan and 12-month arc with quick wins, dependencies, and executive milestones.
Policy posture, audit readiness, and human-in-the-loop checkpoints — defined before the first build.
Proof-of-value design with success metrics, guardrails, integration points, and production criteria set on day one.
Centre-of-excellence design, role definitions, and decision rights — so ownership is explicit before scale.
We set the bar for an advisory engagement using the platforms we run ourselves. These are the targets our own products hit — and the starting point for the ones we set with you.
Most AI advice comes from people who have never run a model in production. Ours comes from designing, building, and operating agentic platforms in regulated environments.
Every recommendation arrives pre-tested against deployment reality — deployment scars, guardrail patterns, and real cost curves rather than analyst reports.
A shipped product behind every vertical we advise. Pilots start from working software rather than a blank repository.
Human-in-the-loop checkpoints, append-only audit, bias and factuality evals, and policy packs — designed in from the first workshop.
Enablement is the deliverable. Your people, your operating model, your capability — stronger after every phase, independent at the end.
Start with one plant, one claims queue, one case unit, or one capture team — and a 30-minute working session. Two to three weeks later: an honest scorecard, a board-ready readout, and a clear decision on what comes next.
Scope a readiness sprint or email info@mteklabs.com