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Last-mile intelligence

Maximize intelligence per token.

Frontier models supply raw cognitive capacity. ogram edge sits on top as the firm-specific layer that makes each token carry more of your sources, methods, controls, and professional judgment.

Where ogram sits

The last-mile layer between frontier inference and expert work.

Codex, Claude, and the frontier model providers supply general capability. ogram edge sits above them as the firm layer: source hierarchy, workflow contracts, review logic, and recovery state.

The result is not a longer answer. It is a denser unit of work: more verified domain intelligence per token, carried into outputs that survive professional review.

Frontier model capability is the base. Last-mile intelligence is the lift.

How it runs

From the working platform to the ogram machine.

The partner keeps working in Codex or Claude. When the question needs specialised depth, ogram runs the dedicated machine and returns the answer.

  1. Step 01Partner

    Work stays in place

    The partner works inside Codex or Claude, in the language and rhythm they already use.

    • Codex
    • Claude
    • Partner seat
  2. Step 02Call

    Specialised request

    When the task needs depth, the platform sends a bounded call to the ogram machine built for that firm and workflow.

    • Scoped task
    • Source perimeter
    • Answer contract
  3. Step 03ogram VM

    Long-running execution

    The dedicated machine runs the specialised work: reads the authorised sources, checks the reasoning, and keeps the task recoverable.

    • Verified
    • Traceable
    • Checkpointed
  4. Step 04Answer

    Precise return

    The answer comes back into the partner's working surface with the reasoning, sources, and limits intact.

    • Precise answer
    • Source lineage
    • Ready to use
The seven failure modes

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.

01
Hallucination

Structural verification and source-grounding at every inferential step. Every number, every citation, traceable to its origin.

02
Memory loss

Persistent state management across extended agent sessions. Nothing is forgotten between the start of the diligence and the final memo.

03
Context rot

Active monitoring and remediation of context degradation over long horizons. The agent that finishes is as sharp as the agent that began.

04
Compaction loss

Preservation of critical information when context windows compress. The facts that matter survive. The noise does not.

05
Agent drift

Continuous alignment between agent behaviour and the original objective. No wandering. No scope creep. No polite deviation.

06
Interruption

Checkpoint, recovery, and resumption of multi-hour workflows. A crashed runtime does not mean a lost afternoon.

07
Orchestration

Coherent coordination of specialised sub-agents working parallel workstreams. One plan, many hands, one output.

Evidence on paper

Every claim leaves an artefact.

Source citations. Long-context traces. Checkpoint logs. The scaffolding produces documents the way a reliable analyst does — because investment-grade work must survive a review.

Source GroundingClaim anchored

The Acquisition Committee notes that consolidated net revenue for the fiscal year ending 31 December 2023 reached CHF 2,341.8 million, representing a year-on-year increase of 7.4% against the prior period. Adjustments under Schedule 4.7(b) reduce normalised EBITDA to CHF 412.6 million.

SHA_Project_Helvetia_v14.pdf
Schedule 4.7(b) · p. 184 · ¶ 3
Exhibit
Extracted passage
…consolidated net revenue for the fiscal year ending 31 December 2023 totalled CHF 2,341.8 million, as independently verified pursuant to Clause 9.3 of the Share Purchase Agreement and confirmed by the statutory auditors' report…
Extracted 2024-03-07 · 11:22:04 CEThash · 3f8a2c1d
01 · Source grounding — claim & anchor
Long-Context FidelityRunning · 5h 33m
Diligence corpus · Project Helvetia · 487 documents · started 09:14 CET
312 / 487
0100200300400487
Drift
0.00
Compaction
14
events, recovered
Sub-agents
7
aligned
Started 09:14:02 CET · 2024-03-0714:47:19 CET · elapsed 5h 33m 17s
02 · Long-context fidelity — coherence over 487 documents
Fault Recovery2024-03-07 · agent.log
14:31:08
Checkpoint 13/22 written
state 4.2 GB · SHA-256 a3f8c2d1
14:31:47
Runtime interrupted
cause: upstream timeout · exit code 124
14:32:14
State restored from checkpoint 13/22
integrity verified · delta 0 bytes
14:32:14
Execution resumed · agent state verified
interruption window: 27s · no re-derivation required
A crashed runtime is not a lost afternoon.
03 · Fault recovery — checkpoint & resume

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.

Discuss your workflow