—Roadmap
How we build, in the open.
Falcron ships production systems, not slideware. This is where we are, what we're building now, and where we're headed — framed as direction, not dated promises.
Foundations in production
The data layer and interfaces our clients run on right now.
- Multi-source data lakehouse — automated bronze → silver → gold pipelines turning raw operational exports into clean, queryable gold tables. 20M+ rows live across deployments.
- Live operational dashboards — decision-first, reading directly from gold, updated on every pipeline run.
- Text-to-SQL / natural-language data access — ask in plain English, get a real answer over the lakehouse.
- Autonomous ingestion & RPA bots — unattended, scheduled, with zero-error production runs.
- Private / local-first deployment — pipelines and agents that run inside the client's own environment when data can't leave the building.
Clients move from manual spreadsheet exports to a single, trusted, queryable source of truth — live, automated, and private.
Production-grade AI ordering agents
Autonomous agents that decide and place stock orders for pharmacies — trained on real operational history and gated by independent verification before a single order is committed.
- Trained on ~20M rows of operational history — dispensing, purchasing, stock movement and expiry data across multiple pharmacy sites — so forecasts reflect how each branch actually buys, not a generic model.
- Per-pharmacy demand & stock-optimisation models — patterns differ branch to branch — project what each line needs over the lead-time window, accounting for seasonality, concession and tariff shifts, and recent dispensing trend.
- Multi-agent juror verification — each proposed order is reviewed by several specialised agents that independently check demand, wholesaler choice, forward-buying logic and margin. An order only proceeds if the panel agrees; no single model can place an order alone.
- Hard guardrails enforced before commit — budget ceilings, expiry / shelf-life limits, shortage and over-order protection, and supplier availability. Any breach blocks the order regardless of agent confidence.
- Human-in-the-loop sign-off with a full audit trail — every order surfaces its forecast, the agents' reasoning and the juror verdict for one-click approval, and every decision is logged immutably.
- Agent observability built in — per-order decision tracing, token and cost accounting, and a record of which agent proposed, which dissented and why.
Ordering decisions that are explainable, reviewable and safe by construction — with a human approving and a complete trail behind every order.
Because every order moves real money and affects patient supply, we never let one model decide alone. Each proposed order is reviewed by several specialised agents — a jury — that independently scrutinise the forecast, the wholesaler choice and the margin impact. The order only goes forward if the panel agrees and every hard guardrail passes; any dissent or breach sends it back or escalates it to a human. Adversarial verification by design: nothing executes unless it survives independent review.
No single model places an order. The panel must agree and every guardrail must pass.
Scaling ordering agents into a platform
Take the verified ordering agents from a single-group deployment to a self-serve platform any pharmacy group can run on its own data.
- Multi-pharmacy orchestration — one control plane running independent ordering agents across many sites and groups, each isolated to its own data while sharing the same verified engine.
- Self-serve onboarding — a new group connects its data and wholesaler accounts, the agents calibrate on its history, and the system reaches supervised ordering in days, not a bespoke build.
- A library of evaluated, version-pinned agents — ordering strategies, juror / verification roles and wholesaler connectors, each scored on back-tested data so operators can see how a component performs before enabling it.
- Expanded wholesaler connectors covering ordering, pricing and availability, so agents can compare and route across suppliers automatically.
- Privacy-preserving cross-pharmacy benchmarking — anonymised, aggregated signals let each group see how its buying, margin and shortage exposure compare to peers, without exposing any other pharmacy's raw data.
A productised ordering-agent platform new pharmacy groups can adopt safely — with benchmarking that gets sharper as more sites join.
We're a small, senior team. We list direction, not delivery dates — and we only move work into Shipped once it's running in production for a real client.
→Ready to ship
Let's get your production AI live.
Bring us your operational data. We'll build the system that acts on it — deployed in your environment, measured in production.