In stealth · Est. 2027

Agents are replaceable. Memory is not.

MiddyMind is the proprietary entity-resolution and memory engine AI agents are built on and run on. Every entity across your systems, resolved. Remembered with provenance. Served to any agent, per turn, inside your own tenant.

Customers bring their own agents - authored in plain language or as their own code. MiddyMind supplies resolution, bitemporal memory, runtime governance, and a live improvement loop the agent consumes but never has to build.

Status
Stealth · pre-seed
Codebase
Monorepo (uv workspace) · in build
Verticals in build
EmpowerReg · ClearedAI · MidnightSprint
Target runtime
Fly.io · in-tenant by default
Applying
MSFT for Startups

TL;DR

The entity-resolution and memory engine under any agent — governed, bitemporal, in-tenant. In stealth, building across 3 regulated verticals. Raising pre-seed.

  • 3
    Verticals in build
    EmpowerReg · ClearedAI · MidnightSprint
  • 6 / 6
    Runtime capabilities designed native
    Only platform scoping Native across all six
  • ≈ 90d
    Target design-partner cycle
    Signed → in-tenant → governed agent
Entity-generic resolutionBitemporal provenancePer-turn Signal BriefIn-tenant federationReversible mergesRTBF cascadeAutonomy tiersLive-eval loopFederated reward modelBring-your-own-agentSigned usage meteringEntity-generic resolutionBitemporal provenancePer-turn Signal BriefIn-tenant federationReversible mergesRTBF cascadeAutonomy tiersLive-eval loopFederated reward modelBring-your-own-agentSigned usage meteringEntity-generic resolutionBitemporal provenancePer-turn Signal BriefIn-tenant federationReversible mergesRTBF cascadeAutonomy tiersLive-eval loopFederated reward modelBring-your-own-agentSigned usage metering
§ 01

The problem

Companies are deploying agents on top of scattered, stateless, unsourced data - and getting confident, ungoverned, unprovable answers at scale.

The failure is structural and lives below the model. It recurs for every agent a company ships.

Failure 01

Fragmented identity

Signal about any entity is scattered across many systems; an agent sees slices.

Failure 02

Amnesia

Agents are amnesic across sessions and channels; what one learned or promised, the next never sees.

Failure 03

No provenance

Answers carry no source; when one is wrong, no one can say which system it came from or when it was last true.

Failure 04

Governance re-litigated

Residency, consent, retention, audit - renegotiated per source and per project instead of enforced once, underneath.

§ 02

The engine

Five primitives. Each a live engine module. The agent is pluggable and lives above this line; everything that makes it useful lives below.

  1. 01

    Entity Resolution

    identity/

    Resolves any entity type across sources.

  2. 02

    Memory Fabric

    memory/

    Bitemporal (valid-time + transaction-time) store of typed, sourced facts.

  3. 03

    Signal Brief

    conductor/signal_brief.py

    A compact ~200-token typed snapshot.

  4. 04

    Governance

    governance/

    In-tenant federation, per-fact attribution, per-purpose consent, three autonomy.

  5. 05

    Live Eval & Improvement

    eval/, learning/

    “The benchmark is yesterday.” Nightly sampling of real interactions.

§ 03

The Conductor

The spine that ties the five primitives together. Every inbound message flows through one ordered lifecycle. Swap the model and the resolved entity graph compounds underneath.

conductor/ · per-message lifecyclelive
identitysignal_briefroutecost capagent callautonomywrite-back
theneval · audit · replay

Two hooks for the agent - read a Signal Brief on demand, write its outcome after. No plumbing. Omnigent swaps Claude / OpenAI / Gemini behind an identical brief.

§ 05

Bring your own agent

Two authoring modes, one engine binding. Both select an entity type and connectors, set policy (autonomy tier + cost cap + consent purpose), and deploy onto the harness in-tenant.

Primary on-ramp

Describe it

A natural-language description is turned into a readable AgentClient subclass by a.

Advanced

Bring your code

Claude Agent SDK, LangGraph, a container image, or a custom HTTP endpoint.

§ 06

In build

In stealth. Verified in code, nothing yet in production. Raising a pre-seed round and applying to Microsoft for Startups to underwrite the next twelve months of infrastructure.

3
Verticals in build
12
Seeded agents across verticals
34+
Registered tools (ClearedAI + MidnightSprint)
5/5
Engine primitives built
§ 07

Competitive analysis

Six runtime capabilities, six categories

Every agent platform must own the six columns end-to-end. We scored the closest neighbors in each category against the same runtime bar we hold ourselves to.

“Coverage” counts only Native marks — Partial means the piece exists but ships as a library, service boundary, or manual step rather than a governed primitive. Hover any cell for the definition.

Closest neighbor

Zep / Graphiti covers 4 of 6 capability dimensions. Strongest single competitor, still not a runtime: no entity-generic resolution, no closed eval loop, no bring-your-own-agent harness.

Agent runtimes

AWS Bedrock AgentCore, LangGraph, Temporal

Entity-generic
Bitemporal
Per-turn brief
In-tenant
Live-eval
BYO agent

Memory layers

Zep / Graphiti, Letta, Mem0, Cognee

Entity-generic
Bitemporal
Per-turn brief
In-tenant
Live-eval
BYO agent

Identity / CDP

Amperity, Salesforce Data Cloud

Entity-generic
Bitemporal
Per-turn brief
In-tenant
Live-eval
BYO agent

CX agents

Sierra, Decagon, Intercom Fin

Entity-generic
Bitemporal
Per-turn brief
In-tenant
Live-eval
BYO agent

Enterprise RAG

Glean

Entity-generic
Bitemporal
Per-turn brief
In-tenant
Live-eval
BYO agent

MiddyMind

This engine

Entity-generic
Bitemporal
Per-turn brief
In-tenant
Live-eval
BYO agent
§ 08

Market opportunity

Three concentric layers — a beachhead procurement will actually approve, the broader agent-infra TAM, and the sovereign health-safety wedge already in motion.

$8-12B
Beachhead — in-tenant AI memory & governance
2028 runtime spend for enterprise + public-sector memory + agent governance.
$45B+
Agent infrastructure & orchestration TAM
Runtimes, memory, eval, and governance across regulated and sovereign buyers.
$450B+
Sovereign health-safety via EmpowerReg
Global health safety intelligence market fronted by US, Turkey, and LATAM partners.
§ 09

Defensibility

Three things competitors cannot copy

MiddyMind is the entity-resolution and memory layer under the agent - complementary to the runtime, identity, and memory tools you already run. The moat lives in three places rivals can’t re-plumb.

  1. 01

    Entity-genericity

    One resolution engine for customers, devices, applications, ideas.

  2. 02

    Governance in-tenant

    The runtime enforces residency, consent and audit inside the customer's perimeter.

  3. 03

    Closed eval loop

    Graded corrections live on the customer's data and feed a federated reward model.

§ 10

How to buy

Deployment postures + pricing shape

MiddyMind is a PaaS you run inside your tenant. Pick a deployment posture below; the pricing shape follows.

Tenant isolation via Postgres row-level security on a per-request tenant GUC. The runtime federation guard refuses to start if the DSN looks like a vendor-owned shared cluster.

Tier 01

Shared-managed

Startups, non-regulated pilots

Our cloud.

Tier 02

Dedicated-in-VPC

Default · regulated enterprise

Runtime federation guard refuses to start against a vendor-owned shared cluster.

Tier 03

Air-gapped / sovereign

Government · defense

Offline install, signed traces only for remote diagnostics.

Pricing shape

PaaS with a design-partner phase

You rent the engine, we run it inside your tenant. Two co-designed usage counters are metered in the runtime today.

Early design partners get the platform floor waived in exchange for a revenue-share agreement on downstream revenue their MiddyMind-powered agents generate. Both sides win as the substrate compounds.

What you pay forDesign partnerNowStandard PaaSAt GA
Platform floorWaived. No annual fee during the design-partner phase.Annual fee by deployment posture — shared, in-VPC, or air-gapped.
Usage — resolved entitiesNot metered. You keep everything you build.Metered per entity under management.
Usage — agent-turnsNot metered.Metered per turn served.
Revenue shareYes. Agreed % of downstream revenue, fixed term.None. Floor + usage only.
CommitmentNamed design-partner agreement, joint roadmap, quarterly reviews.Annual contract, standard SLAs, self-serve expansion.
Billing evidenceSigned, tamper-evident cost ledger emitted by the runtime — verifiable on your side without breaking tenant federation.

Why this shape: design partners take real risk with us early, so we take revenue-share risk with them - no platform bill until their agents earn. At GA we drop the revenue share and switch to a transparent floor + two usage counters.

Rejected: per-seat (punishes the “substrate fades” outcome), per-connector (taxes the wedge), pure per-token (positions us as a model reseller).

§ 11

Go-to-market plan

Three sequenced phases: convert the signed sovereign pipeline, template it, then open the standard PaaS with a federated loop.

  1. Phase 01Now - 6mo

    Convert the signed sovereign pipeline

    Ship in-country production deployments through EmpowerReg's regional partners.

  2. Phase 026-18mo

    Templated sovereign verticals + Ship Gate benchmarks

    Package each jurisdiction as an eval-backed template so new regulators deploy in days.

  3. Phase 0318-36mo

    Standard PaaS at GA + federated sovereign loop

    Convert design partners to floor-plus-usage pricing.

§ 12

Investors & funders

Sovereign-grade memory + governance, with a signed nation-state pipeline via EmpowerReg. Three regional partners — Network Partners (US), Excopan (Turkey), Mandala (LATAM) — are already fronting device regulators, hospital systems, and ministries of health in a $450B+ safety-intelligence market.

Written for two reviewers: VC partners underwriting a defensible substrate, and cloud program leads underwriting sovereign / in-country hyperscaler consumption. Traction, sovereign channels, market, timing, and sequenced GTM below.

Traction — defined, dated, sourced

Each metric below states its as-of date, an explicit definition of what counts, and a target with a confidence band. Nothing here is a projection dressed as a fact.

3
Verified
Signed design partners
As of Q3 2026
Definition
Countersigned design-partner agreement with a named executive sponsor and an active technical integration channel. Excludes LOIs, warm intros, and pilots without a signed contract.
Target / confidence
Target 6 by Q2 2027 · confidence high (3 in advanced conversation).
Source
EmpowerReg, Cleared AI, MidnightSprint.
3
Verified
Sovereign / nation-state channels engaged
As of Q3 2026 · via EmpowerReg
Definition
A regional partner with an existing book of business into a national regulator, ministry of health, or public health-safety authority — reached through EmpowerReg's countersigned reseller agreements.
Target / confidence
First in-country deployment target Q1 2027 (US) · confidence high; Q2 2027 (Turkey, LATAM) · confidence medium.
Source
Network Partners (US), Excopan (Turkey), Mandala Group (LATAM).
6 / 6
Verified · see Section 08
Runtime capabilities designed native
As of current build
Definition
Native = built as a first-class, governed primitive in the product (not SDK glue or a sibling service). Scored across the six-capability matrix in Section 08.
Target / confidence
Third-party benchmark publication vs Zep / Mem0 / Cognee targeted Q4 2026 · confidence medium (dependent on benchmark harness).
Source
Only platform scoping Native across all six dimensions today.
≈ 90 days
Target range 60-120 days
Target design-partner cycle
Planned across current 3 partners
Definition
Signed agreement → in-country VPC deployment → first governed agent running against real customer data with per-fact provenance and eval instrumentation.
Target / confidence
Target 45 days once vertical templates ship (H2 2027) · confidence medium.
Source
Reference architectures are reused across partners in the same jurisdiction.

Confidence · High = signed / observed · Medium = active pipeline · Low = plan of record only.

Sovereign pipeline via EmpowerReg

EmpowerReg is a US-based global health safety intelligence company already selling into device makers and hospitals through an AI-native SaaS platform for safety, risk intelligence, regulatory / compliance workflows, and post-market surveillance. MiddyMind is embedded as the memory + governance substrate under that stack — which means EmpowerReg's three regional partners double as MiddyMind's sovereign-tenant channel from day one.

United States
Network Partners Group

FDA-facing device makers + hospital systems.

Türkiye
Excopan

Turkish Medicines and Medical Devices Agency (TİTCK) ecosystem.

LATAM
Mandala Group

ANVISA (Brazil), COFEPRIS (Mexico), INVIMA (Colombia) and adjacent authorities.

Why now
01

Sovereign AI procurement is compressing

Ministries and regulators are standing up in-country AI mandates now.

02

Agents commoditize on a 12-18mo clock

Durable value moves to memory, governance, and eval underneath.

03

The closed eval loop is buildable

Federated learning on graded corrections is shippable today.

The ask
VC / Angel

Pre-seed round — 18-month runway

Funds the engineering to convert the signed sovereign / design-partner pipeline into.

Cloud program

Azure — startup

Underwrites in-country tenant infrastructure across every EmpowerReg regional.

The compounding argument: agents are replaceable, memory isn’t. Every source a customer connects, every correction they grade, every entity they resolve makes the substrate more valuable - to them and to every partner on it. That’s the asset both VC and cloud capital are underwriting.

§ 13

Our team

Operators who have shipped in regulated markets, now building the memory substrate the agent economy runs on.

Co-founder
Anu Trivedi

Enterprise AI, product & engineering.

Co-founder
(Stealth)

Enterprise data & AI, go-to-market.

The team
  • Yang Yang · Founding AI Scientist
  • Madhavi Sharma · Founding Engineer
Hiring
  • 2 Engineers
  • 2 Research Scientists
End of brief · The ask

Agents are replaceable.
Memory is not.

If you’re an investor, a design partner shipping agents into a regulated tenant, or a program office evaluating in-tenant AI infrastructure — we’d like to hear from you. Everything else can wait until we’re out of stealth.

Under NDA · signed traces only
anu@middymind.ai

Every intro reply includes a signed traces bundle keyed to your deployment posture. Standard reply window: 24 hours.

For
Investors
Pre-seed round open
For
Design partners
Rev-share, floor waived
For
Program offices
Sovereign / regulated
For
Cleared talent
Selective hiring
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