The machine behind decision-grade agents.
Not a chat wrapper. Not a prompt collection. A reliability architecture for long-running analytical work where the output must survive scrutiny, interruption, and downstream use.
Decision set, governing question, answer contracts.
Targeted branch dispatch with explicit return packets.
Source to passage to evidence to claim to proof point.
Structured bundles that can render into deliverables.
From the partner's brief to a decision-grade deliverable.
Four stages. One continuous thread of mandate, context, and accountability — held by the machine from the first word to the final page.
- Step 01Partner
The briefing
The partner frames the question in their own language. What decision is on the table, what cannot be wrong, what must be ready by when.
- Mandate
- Decision set
- Timeline
- Step 02Analyst pod
Scope & sources
Analysts wrap the brief with the exact surfaces ogram is authorised to touch — drives, SharePoints, data rooms, LSEG, Bloomberg, internal lakes — and the shape of the deliverable expected.
- Access grants
- Source allowlist
- Answer contract
- Step 03ogram
Long-running execution
The machine runs guardrailed, reliable, multi-hour work. Every claim grounded in a source, every step checkpointed, every sub-agent aligned to the original question.
- Guardrailed
- Traceable
- Checkpointed
- Step 04Deliverable
Decision-grade output
The result lands ready to use — memo, model, deck — with full proof lineage intact. Built for the committee, not for the editor.
- Ready to use
- Full lineage
- Decision-grade
Agentic AI breaks where the stakes are highest.
Hallucination, memory loss, context rot, agent drift. These are not edge cases. They are structural failure modes that make current harnesses unsuitable for investment-grade work. ogram addresses each one architecturally.
Structural verification and source-grounding at every inferential step. Every number, every citation, traceable to its origin.
Persistent state management across extended agent sessions. Nothing is forgotten between the start of the diligence and the final memo.
Active monitoring and remediation of context degradation over long horizons. The agent that finishes is as sharp as the agent that began.
Preservation of critical information when context windows compress. The facts that matter survive. The noise does not.
Continuous alignment between agent behaviour and the original objective. No wandering. No scope creep. No polite deviation.
Checkpoint, recovery, and resumption of multi-hour workflows. A crashed runtime does not mean a lost afternoon.
Coherent coordination of specialised sub-agents working parallel workstreams. One plan, many hands, one output.
The machine,
then the adaptation.
A general-purpose reliability engine that scores at the top of financial AI benchmarks out of the box. Then the compounding advantage: it adapts to your team, your positioning, your know-how, your view of a good comp set.
Sandboxed deployment of agentic runtime. Swiss jurisdiction, Swiss data protection, Swiss discretion. Built from the ground up for clients where sovereignty is a requirement, not a feature.
- Isolation and tenant-level sandboxing
- Full audit trail of every agent action
- Model-portable — no provider lock-in
- Enterprise-grade security posture
The scaffolding layer that addresses the seven failure modes of long-running agentic tasks. Purpose-built for investigation and reporting in highly specialised domains.
- Persistent memory across multi-hour sessions
- Source-grounding and verification at every step
- Checkpointing and recovery from interruption
- Coherent orchestration of specialised sub-agents
Built for work that continues after the first answer.
Investment-grade AI is not a single generation problem. It is a control, memory, and verification problem carried across the full life of the case.
See cases in practice→