Enterprise AI in weeks,
not years.
Traditional IT: 6-18 months. Big-4 consulting: 12-24 months. Orqent Labs: production in 2-6 weeks. Here is exactly how.
- 01
Discovery & Scoping
Day 1-2Requirements locked, architecture defined, team assigned. Stakeholders aligned.
- 02
Proof of Concept
Day 3-5Working prototype delivered, live demo ready for client. Decisions made on real artifacts.
- 03
Pilot Build
Week 2-3Core system live, iterated with real client feedback, real data, real workflows.
- 04
Integration
Week 4CRM, ERP, API, and cloud connections complete. Identity, data, telemetry wired.
- 05
UAT & Training
Week 5User acceptance testing and staff onboarding complete. Adoption plan in motion.
- 06
Go-Live
Week 6Full production deployment, monitoring active, SLAs enforced from minute one.
- 07
Post-Launch Optimization
Week 7+Continuous improvement, scaling, model upgrades, cost tuning, without re-platforming.
The definitive choice for
enterprise AI at global scale.
Multi-Model Intelligence
60+ frontier models orchestrated simultaneously, matched to every task, domain, and compliance constraint.
Unmatched Deployment Speed
POC to production in 6 weeks. While competitors plan, your clients are live and scaling.
Global Enterprise Ready
Multi-region, multi-cloud, multi-language, enterprise SLAs and 24/7 dedicated support.
Compliance-First
Built with regulatory compliance at the core, 50+ frameworks across every major industry.
World-Class AI Products
Froice, PVRep.ai, and a growing suite of proprietary platforms trusted globally.
Speed Without Compromise
Agile delivery, enterprise quality. We move at AI speed, never at the expense of security.
What the six weeks actually contain
A schedule is only credible if it says what happens in each week and what would break it. Here is both.
Six weeks is not a discount on scope. It is a consequence of scoping honestly. Most enterprise AI programmes do not run long because the engineering is hard; they run long because the first eight weeks are spent deciding what to build, and the decision is made by people who are not the ones building it.
We compress that by starting from a measurement rather than a workshop. In week one we establish the current cycle time, touch count or error rate for the workflow in question. That number does two things: it tells us whether the project is worth doing at all, and it becomes the thing we are measured against at the end. If the number says the automation is not worth building, we say so in week one. That is a better outcome for you than a polished six-week build of the wrong thing.
- Week 1Measure & scopeBaseline the workflow, cycle time, touch count, error rate, cost per transaction. Map the exceptions, not just the happy path. Confirm the data exists and is reachable. Output: a scope with a number attached to it.step 1
- Week 2Integrate firstConnect to the systems of record before building any intelligence on top. Authentication, permissions, read paths, and the write path behind an approval. A model that cannot reach your systems is a demo, so this comes before the model work.step 2
- Week 3Thin sliceThe narrowest end-to-end path that a real user can complete, with everything uncertain escalating to a human. Deliberately unimpressive and deliberately real. It is in front of users this week, not at the end.step 3
- Week 4Widen & evaluateAbsorb the highest-volume exceptions. Build the evaluation set from real domain questions with verified answers, and wire it into CI so every later change is checked against it.step 4
- Week 5HardenGuardrails, rate limits, cost ceilings, observability and the audit trail. Adversarial testing, prompt injection, tool misuse, data leakage. Load-test against your actual peak rather than an average.step 5
- Week 6Hand overProduction deployment, runbooks, monitoring dashboards and a walkthrough with the team who will own it. We re-measure the week-one baseline and report the delta, whichever way it went.step 6
- What we need from you
- One decision-maker who can approve scope without a committee, one person who knows the workflow, and system access by end of week one. Those three things account for most of the variance in delivery.
- What makes it slip
- System access arriving in week three rather than week one. Scope that grows because the measurement was never agreed. A stakeholder who first sees the work in week five.
- What we do in week one if it is not worth building
- Tell you, with the numbers. You pay for the week, not the six.
- After handover
- Your team owns it and can run it. That is what the runbooks and tests are for. A managed retainer is available, never assumed.
Where six weeks is the wrong promise
- A multi-department programme spanning several workflows. We stage it instead, so the first workflow is live while the rest is still being built, but the whole is not six weeks.
- A build blocked on data that does not yet exist. Collection has its own timeline and no amount of engineering speed compresses it.
- Anything requiring formal validation before production, GxP, or a regulator's sign-off. The engineering may take six weeks; the validation does not.
- A migration off a legacy system nobody currently understands. Reconstructing that behaviour is the project, and it is measured in months.