Enginev0.4.2
Retrieval96 ms
Quality≈ 88%
Tokenslower
Partnersopen
Deployin-tenant
01 Platform
An operational memory layer for agentic systems.
Live operational
profiles for
AI agents.
E&V turns calls, chats, docs, CRM records, tickets, and code activity into live profiles of customers, projects, cases, and teams — so agents act with continuity, not guesses.
Not a data warehouse. Not a knowledge graph. The structured memory layer between raw data and agent action.
02 The problem
Agents have access. They still don't have state.
01Fragmentation
State lives in too many systems.
Customer, account, and project context is scattered across calls, Slack, docs, tickets, CRM, and code. No single system holds the full picture.
02Brute force
Context stuffing doesn't scale.
Dropping entire conversation histories into context windows is slow, expensive, and still fails to produce durable understanding.
03Unstructured memory
Most memory layers store text.
They don't compile who, what, when, why, what changed, and what to do next. Agents receive passages where they need state.
04Manual reconciliation
Operators cross-reference by hand.
Before an agent can take a useful action on a real account, someone still stitches the picture together across four tabs.
03 Platform
From raw history to usable profiles.
Profile types
Type01
CU
Customer
Identity, contact history, preferences, relationships, open issues, last interaction.
Type02
AC
Account
Stakeholders, contract state, usage trends, risks, tickets, calls, next actions.
Type03
PR
Project
Goals, owners, decisions, blockers, files, timeline, unresolved questions.
Type04
CA
Case
Facts, documents, parties, obligations, deadlines, evidence trail.
Type05
CB
Codebase
Architecture, components, symbols, PRs, branch history, team activity, blockers.
ev / accounts / acme-corp / profile
Live · updated 14m ago
Evidence23 sources · 4 systems
CallGong · Apr 14
Sales · renewal talk.
"Moving to annual plan, need SOC 2 before Q3."
ChatSlack · Apr 12
Champion raised concern about API rate limits on prod during rollout window.
TicketZendesk #4821
Billing issue resolved. Account switched to invoice pay, net-30.
CRMNote · Apr 10
Champion is VP Eng, reports to CTO. Procurement signed off verbally.
TelemProduct · 30d
Usage up +34% MoM. Active seats 12 → 19. Prod envs active.
ThreadEmail · Apr 8
Legal requested DPA amendments. Redlines pending internal review.
A
Acme Corp
Expansion · moving to annual ent_01HXQ9ZKM3 · since 2023-04-07 Champion
Jordan LeeVP Engineering · reports to CTO
Usage
19 seats ↑ 58% from 12Usage +34% MoM · prod envs active
Open risks
SOC 2 required before Q3 close
API rate limit concerns on prod
Relationships
Jordan Lee → CTO
Billing → Finance
Engineering → Support
Billing → Finance
Engineering → Support
Last touch
Call · Apr 14 · 38mNext scheduled: QBR, Apr 29
Compiled from 23 sources across 4 systems.
Profile confidence 0.92 · high
Profile confidence 0.92 · high
04 How it works
Ingest. Compile. Serve.
Pipeline
Stage 01 — Ingest evidence
Connect the systems you already run.
Operational data sources — calls, chat, docs, CRM, code, tickets, warehouse events. Indexed across semantic, lexical, entity, and temporal signals.
GongSlack SalesforceZendesk GitHubNotion + 32 more
Stage 02 — Compile profiles
Structured state, not summaries.
Extract entities, map relationships, preserve attribution, weight by recency, confidence-gate evidence, compile structured profile objects.
Entity resolutionGraph edges ProvenanceRecency weighting
Stage 03 — Serve to agents
Current state, by API, in milliseconds.
Expose live profiles to agents, operators, and workflows. Profiles update as new evidence arrives — agents always get current state.
RESTgRPC MCPSDKs · py, ts, go
05 Use cases
Where it works. Every workflow has entities and relationships.
Workloads
01
CRM & SalesRevenue teams
Account profiles compiled from calls, emails, CRM notes, meetings, and usage events. Agents know who matters, what changed, and what to do next — before the rep opens the CRM.
Read →
02
Customer supportTicket ops
Case profiles from tickets, conversations, product telemetry, and prior escalations. Agents arrive with the full history, not a keyword search against last week's tickets.
Read →
03
Engineering teamsPlatform & infra
Codebase and project profiles from coding sessions, PRs, branches, files, decisions, and blockers. Agents understand architecture and team context, not just file contents.
Read →
04
Enterprise operationsInternal platform
Operational profiles from tickets, docs, warehouse events, approvals, finance, and internal communication. The connective tissue across fragmented enterprise systems.
Read →
06 Stack position
Built to sit above what you already have.
Coexistence
- Works above warehouses and SaaS systems
- Works beside existing agent frameworks
- Uses frontier models for structured readout
- Preserves source attribution and provenance
| Layer | What it returns | What agents still need |
|---|---|---|
| Data warehouse | Rows and events | Interpretation |
| Vector search / RAG | Relevant passages | State compilation |
| Knowledge graph | Nodes and edges | Context and evidence |
| E&V | Live operational profiles | ◆ Ready-to-use structured state |
07 Engine
Built for state, not maximum context.
Design
Hybrid retrieval
Semantic, lexical, entity, temporal, and attribution-aware retrieval in a single pass.
Smart evidence selection
Field-specific retrieval, deduplication, recency weighting, confidence gating.
Token discipline
Profiles compiled from selected evidence — not brute-forced context windows.
CPU-first retrieval
Retrieval path runs efficiently on CPU before the model is called.
Internal benchmarkBryonics · v0.4.2
Median CPU retrieval latency
96ms
Profile quality at lower token costvs. baseline RAG · internal eval set
≈ 88%
Post-hoc extraction precisionfields verified against source
◆ Verified
Evidence attribution coverage
100%
08 Next
Your agents have access. Give them profiles.
We're working with design partners who need better operational profiles across customers, accounts, teams, projects, and cases.