One context graph per industry. Every agent that runs on top of it cites the regulation, standard, and benchmark it leans on.
Language models know language — not your operations. Generating the agent is the easy part now. Grounding it in an industry's real operations — every process, rule, regulation, and benchmark — is what nobody else has built. Amandil is that graph: the substrate agents query before they answer.
A healthcare agent's answer is either grounded in the graph or it isn't. Here's the difference, side by side — same question, one model bolted to a workflow, the other querying the Operational Context Graph before it speaks. Healthcare shown; the same pattern instantiates against the standards and regulations of every vertical — see §04 Pattern.
A health system uses it in-house. A GSI uses it on behalf of a customer. A platform vendor uses it inside their own product. All three ship agentic AI fast.
Pick by benchmark, not vendor pitch. Start at the CMS gap, at the automation score across every process, or by describing the problem in natural language. The graph brings the right components to bear. Defensible on day one. Scoreable on day ninety.
'Prior authorization: 5x projected ROI vs the other six we ranked — closes a CMS interoperability gap the new mandate already requires us to address.'
Massively compress the discovery phase — start at the gap, inject context, build the blueprint, validate against evals, generate the code. Prior authorization, already encoded with every standard, role, regulation, and benchmark. Done once. Yours in days. The spec is production-grade.
'Six weeks of discovery in days. The blueprint validated against our evals — runbooks, success criteria, HITL spec, integration map — and tied to a closeable CMS benchmark gap.'
The agent is yours — and it runs where you run. Build once, deploy to your own cloud or any of six runtimes. It stays wired to the graph via MCP — the connection that keeps it grounded as the industry itself evolves: operations, standards, regulation, benchmarks, all of it. You own the agent; the graph keeps it current. An agent frozen at build time goes stale; one on the graph moves with the industry.
Most agents ship as islands. Built on Amandil, an agent has the graph behind it — failure modes, state machine, every standard and regulation. The graph stays in the loop, tuned as the reference evolves.
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The graph and its agents are accessible via REST, MCP, or direct embed. Same shape, every vertical — switch the tab to see the call land in healthcare, financial, energy, or retail. No fine-tuning. No vector store. No prompt engineering against PDFs.
The agent calls the graph the way it would call any other tool. The graph returns structured, token-budgeted operational context — typed nodes, typed edges, citations, sources, peer benchmarks. The agent reasons on the structure, not on the corpus.
vertical: key — the response shape doesn't change.Public-data spine, reference standards, regulatory frameworks, canonical operational example — the dimensions are identical. The substance is per-vertical. Healthcare is live; financial, energy, and retail follow.
Three customer segments — the same across every vertical. The org that runs the operations. The firms that consult into them. The vendors that embed Amandil in their own products.
“Where should we invest in AI?” You run the operations. Most teams pick what to automate based on a vendor pitch or what feels problematic to an executive. You need a benchmark-anchored answer — and agents that cite the regulation, name the process, and return the audit trail.
You get: diagnose against the benchmark library → prioritize by automation score reweighted by your gaps → simulate the top candidates → ship agents grounded in the graph via MCP.
“How do we scale our AI engagements?” Every project opens with a discovery phase. Weeks of work to rebuild what's broadly the same in every operation. The sameness goes unclaimed — and the artifact ages on contact with reality.
You get: the discovery phase compressed to a 30-minute branded analysis. White-label per client. Branded blueprints, business cases, diagnostics. Multi-client workspaces with scoped grants. The Agent Pipeline as the consulting workflow re-imagined.
“What grounds our AI features?” You're shipping AI inside your product. Every LLM-integrated feature hits the same wall — the model has no grounded knowledge of the operation. Building the graph yourself is a multi-year, multi-million-dollar undertaking.
You get: a native MCP server (13 tools, 3 read-only resources). OAuth 2.0 plus headless bearer tokens, instantly revocable. Grant-scoped per partner. Versioned, validated graph surface. Multi-year build, solved on day one.
Healthcare is live. Financial, energy, and retail are in design-partner cohort. Tell us your industry and your operational question — we'll send back a roadmap.
If you're in healthcare: we'll send back a roadmap with your public CMS footprint, your peer cohort, and the agent surfaces we'd deploy first.
If you're in finance, energy, or retail: design-partner cohorts are open. You get the first live graph in your industry; we get the operational depth to make it real.
If you're an applied-AI team: we'll get you a sandbox MCP key and the integration patterns within a week.
Press, academia, builders, the curious: just say hi. hello@amandil.ai