DIH AI OS · Executive Update
The DIH AI OS unifies Atlas, Hub, and Email Triage on a single governed intelligence pipeline — turning every email and document the firm receives into permanent, permissioned, cited institutional memory. Keystone, and every domain agent it spins up, will run on this one unified memory layer.
11 June 2026
Where We Left Off
Three agreements set the direction. This update reports what was built on them.
Context first
The models need structured context — pipeline data, defined personas, curated entities — so the system stops guessing and starts knowing.
Governance first
Access controls, document protection, and audit logging form the foundation — trust built in before scale, not retrofitted after.
The AI OS is the critical path
One platform binding Atlas, Hub, and email into a single system — and, on your read, the seed of the firm's project pipeline. Design first, then build.
The Mandate
The next logical step after three proven apps: one institutional memory that feeds every app — and every agent after it.
Today, every system the firm runs reads the same mail and reaches its own conclusions. The unified AI OS inverts that: a single governed pipeline reads every source once, distills it into scored, permissioned facts, and lands them in one memory that every application — and, in time, every agent — draws on.
The architecture is fully documented — covering ingestion, entity resolution, confidentiality, data ownership, and the transition path for the current apps. Nothing here is a sketch — it is a ratified design with a phased build plan behind it.
And the memory compounds. Every deal screened, every model reviewed, every negotiation thread becomes queryable, cited evidence — the firm's accumulated judgment, available to every future deal team from day one.
The four layers of the DIH AI OS — each layer serves the one above it.
The Current Apps
All three move onto the unified AI OS — re-seated, not replaced.
Three independent AI pipelines
Atlas, Hub, and Email Triage share a database instance, but each runs its own independent AI pipeline. The same email is read three times, interpreted three ways, and remembered nowhere. The capability is proven; the intelligence is siloed.
One pipeline, three apps, one memory
The unified feeder reads each source once and serves all three apps from one governed memory. Each app keeps its job — Atlas the relationship graph, Hub the document home, Triage the inbox — and each switches over only when the unified pipeline proves more accurate than its own. The same memory then serves Keystone, the orchestrator, which spins up domain-specific agents — all reasoning over the one governed record.
The Centerpiece
From the moment an email arrives to the moment it lands as governed, cited knowledge — automated end to end, audited at every step. This is how the memory gets built.
Why This Wins
The moat is not the apps — it is the governed memory beneath them.
Ingest broadly, expose narrowly
Full email and document content is ingested, but every derived fact inherits the strictest permissions of its sources. No summary is ever readable more broadly than what it was built from — MNPI-safe by construction.
Accuracy gates
Each app moves onto the unified memory only when the new pipeline measurably beats the app's own results on adjudicated, side-by-side comparisons. Quality is gated, not assumed.
The feedback flywheel
Every human accept, reject, or correction in any app flows back into the memory and recalibrates it daily. The system the firm uses is the system the firm trains — a compounding asset competitors cannot copy.
Full provenance
Every fact carries its complete chain of evidence — which email, which version, which model, which human confirmed it. Audit, eDiscovery, and "why do we believe this?" are answerable in one query.
How We Build
Autonomous build–verify–correct loops run many workstreams in parallel — the same step-change we are delivering for the firm is the one we use to build it.
How software was built
Manual coding
Engineers write every line by hand. Throughput is bound by headcount and hours; quality is bound by review capacity. One workstream per engineer, sequential by nature.
PaceBound by typing speed — weeks per feature.
How most teams build today
Agentic coding
Engineers prompt an AI agent task by task. The writing is faster, but every step still waits on a human instruction — one conversation, one thread of work at a time. The bottleneck moves from the keyboard to the prompt.
PaceBound by prompting — days per feature, one thread.
How we build
Agent-loop development
Autonomous loops plan, build, verify, and correct continuously. Engineers set direction and review decisions, not keystrokes — many workstreams run in parallel, each verifying its own output before a human ever sees it.
PaceParallel loops, continuous verification — a platform in weeks.
Roadmap
Phase 1 is scoped, designed, and ready — the clock starts when development begins.
The unified pipeline — and everything agreed
~3 weeks from development start
The feeder pipeline live end to end; Atlas, Hub, and Triage re-seated on the unified memory; the five registers populated and steward-curated; entitlement, audit, and the feedback loop running from day one.
The agents move in
Follows Phase 1 — on top of live memory
Keystone, the orchestrator, routes work to specialised subagents — deals & diligence, legal, investor relations, portfolio, and the firm's internal functions. Each agent reads the same governed memory and never exceeds the access of the person it serves. Phase 1 is what makes Phase 2 possible: agents are only as good as the memory they stand on.
Next Steps
Two active mandates — Heirloom and Mesec — run end to end through the full pipeline.
Demonstration 1
Heirloom
Live data from ~February 2026
The full feeder pipeline run over the Heirloom correspondence and documents — watch real emails become scored, permissioned facts in the registers and surface in all three apps.
Demonstration 2
Mesec
Live data from ~November 2025
A deeper history: seven months of Mesec material through the same pipeline — demonstrating backfill over a longer window and a register that reads like the deal's institutional memory.
What the demonstration covers
All live data available across the unified registers at the demonstration.
Design mockups — opens in a new tab.
DD Workbench: https://dih-demo.augmentgroup.ai/dih-dd-workbench.html