A full system where AI does the work: agents in production, RAG over your documents and data, a multi-agent architecture, built and run with you under one PMO. For work that manual effort made too expensive to attempt, until now.
Talk to our AI teamPoint solutions do not scale. By the time a team reaches this stage they have usually tried chatbots, added a copilot or two, and run a PoC, and the result is AI bolted onto existing systems: fragile in production and impossible to govern as one thing. Building a system where AI is genuinely the core, not a layer on top, takes a different team and a different engagement model. Vendors deliver code and disappear. Partners stay accountable for the outcome.
Agents running in production, a RAG layer over your documents and data, a multi-agent architecture on MCP (Model Context Protocol, the standard for how agents share context and coordinate), and the data engineering underneath built for AI consumption from the start, not retrofitted from a BI pipeline.
This is where new capability comes from: agents that turn thousands of unstructured clinical documents into structured, reviewable output is work that was never done before, because doing it by hand was not economically possible.
The part most teams underestimate is the delivery framework. One unified PMO. Quality gates every sprint. End-to-end traceability from requirement to release. 21 CFR Part 11 built into the workflow rather than documented after the fact.
We work as a partner: the PMO is shared, both sides see the same risk register and the same delivery status, and we stay accountable for governance, adoption, and outcomes, not just for shipping code. The build is staffed by Appsilon AI engineers, not prompt engineers or Shiny developers asked to stretch into the role.
Workflows that were not possible by hand, because manual effort made them too expensive. When AI is the engine, the work itself changes.
A capability built into the core of how you operate is one competitors cannot copy by buying the same tool.
Specialists spend their day on the cases that need human expertise, not on triage and reconciliation a system can handle.
The same agent and retrieval components extend to adjacent workflows through configuration, not a new project.
Requirements are built with your experts and team leads in the room, not handed to us in a spec, so the backlog matches how the work actually runs. We design a modular, scalable architecture that is compliant from day one (GxP, 21 CFR Part 11, ALCOA+) and consolidates the five-to-seven tools a typical team strings together into one platform. The output is a prioritized, traceable backlog and an architecture you can defend to QA and to your steering committee.
Each sprint has quality gates: peer reviews, validation checkpoints, automated tests, and documented sign-offs. End-to-end traceability runs from requirement through design and code to release, so any feature traces back to the request that prompted it. Audit-ready documentation is produced as the build runs, not assembled at the end under deadline pressure.
Rollout is built around the people doing the work: simplify the job, do not stack a new tool on top of the old ones. Role-based enablement and short proof-of-value sessions are designed to surface measurable time savings within the first weeks.
Three tiers run through the engagement. Oversight is shared on purpose, both sides see the same status, the same risks, and the same decisions. We stay accountable through adoption. We do not ship and disappear.
A global pharma client, regulated expert-review workflow.
The workflow had become a manual bottleneck. Specialists were pulling fragments across five-to-seven separate tools, reconciling exports, and rebuilding context by hand, which left most of their time going to triage instead of expert judgment. Problems surfaced late in the cycle, when fixing them was expensive.
A full AI-core system: a RAG layer over the relevant documents and data plus a multi-agent architecture, consolidating the prior tool stack into one platform. Agents handle retrieval, structural checks, and surfacing; the specialists spend their time on the cases that need human judgment. Validation, traceability, and 21 CFR Part 11 controls are built into the platform, not added afterward.
At this scale, a full AI-core system in regulated work, auditability and control are the foundation, not an add-on. Every agent action, every model version, every human approval, every tool call lands in the record. When QA or a regulator asks why a decision was made, the answer is already there, and it holds up under the same scrutiny as the rest of your regulated stack.
Our Validated AI approachA full system where AI does the work, built and run with you under one PMO, with validation and traceability in from day one.