A two-week engagement that maps your workflows, surfaces high-impact AI use cases, and quantifies where automation creates the most value — before a single line of integration code is written. The right AI projects begin with the right questions.
Pilots stall, budgets vanish, and credibility erodes — not because the technology doesn't work, but because the wrong workflows get automated, the right ones get missed, and use-case development is rarely done systematically.
Most organisations don't know what they don't know. Workflows designed before the current AI inflection rarely survive contact with it — many require rethinking, re-imagining and re-engineering before they're worth automating at all.
Discovery is the work that has to happen first. We do it in two weeks — so the build phase that follows is fast, scoped, and grounded in business value.
Pattern observed across defense, government, energy and infrastructure deployments
The first idea on the whiteboard is rarely the highest-impact one. Without systematic discovery, organisations spend a year automating a workflow that mattered before — not the one that matters now.
Most processes were built for human throughput, not agentic execution. They need to be re-thought, re-imagined and re-engineered to actually take advantage of recent AI capabilities — not paved over with a chatbot.
Internal teams are too close to the work to see the leverage. External vendors are too far from the operations to see the constraints. Discovery sits in the middle — operator-grade interviews paired with what's actually possible today.
Every failed pilot makes the next one harder to fund — internally with sponsors, and externally with regulators and the public. The cost is rarely just the budget burned. It's a year of organisational AI readiness, traded for a presentation deck.
Five workstreams, run in parallel by L19 strategists and engineers alongside your operators and process owners. By the end of week two you have a prioritised list of use cases, a quantified value model, and an implementation plan our engineering teams can pick up the next day.
We embed with your team to document critical processes across departments — from intake to decision to action. We surface bottlenecks, repetitive tasks, decision points, and the silent costs that don't appear in any dashboard.
Through workshops and structured working sessions, we generate candidate AI use cases against your real processes — then score each against feasibility, impact and strategic fit. The output is a ranked list of opportunities, not a hopeful longlist.
For the top opportunities we build a concise business model — estimating efficiency gains, cost savings, risk reduction, or new revenue. Every recommended initiative leaves discovery with a clear value proposition that finance and operations both recognise.
We examine data availability, system readiness, organisational alignment and clearance posture. We then outline the high-level governance scaffolding required to develop AI responsibly, sovereignly, and within whatever regulatory frame you operate under.
You leave with a tailored roadmap: which use cases to tackle first, what resources you'll need, what the first 90 days of implementation look like, and a clear vision for how AI will reshape your operations over the next 12–24 months. Build-ready on day one.
Not a slide deck. Not a vendor pitch. A working artefact your team — and ours — can pick up and execute against.
Each scored on impact, feasibility, and strategic fit — defensible to a board, actionable by an engineering team.
Efficiency, cost, risk reduction, or new revenue — modelled with sensitivity ranges your CFO can interrogate.
Where you can move now. Where pre-work is required. What's blocked, and what it would take to unblock it.
Specific to your sector and jurisdiction — not a generic AI ethics deck pulled from a consultancy template.
Build-ready on day one. Designed to be picked up by L19 engineering or your own teams without a re-baseline.
Most discovery engagements end with a deck and a vendor RFP. Ours ends with a roadmap that flows directly into MESH Agents — implemented by the same L19 engineering teams that scoped it. Nothing is re-discovered. Nothing is re-explained. Build starts on day one of week three.
Workflows mapped. Use cases scored. Value modelled. Governance scaffolded. Roadmap signed off by sponsor and operators.
The L19 strategists and engineers who ran discovery are the ones who pick up the build. No vendor swap. No re-baseline. No re-explaining your business.
Agents deployed against the prioritised use cases — sovereign, on-prem or sovereign cloud, audit-graded, human-in-the-loop.
See MESH Agents →Discovery Intelligence is built for executives, process owners, and innovation teams who want to understand the value of AI automation in their organisation — without committing to a full implementation up front.