Private · Verified · Done for You

The brain your company never had —
a private AI operating layer for your team.

"Where did we land on that?" "Who made that call?" "Wait — when did that change?"

Your entire company's knowledge, verified and searchable — running on a private server built only for you. No shared infrastructure. No exposure. One brain, fully yours.

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The problem

Teams run on knowledge that is scattered, unorganised, and impossible to find.

What a team knows is buried across Slack threads, email chains, documents, and people's heads — unorganised, rarely searchable, and constantly costing time. Jumping between tools to find a decision made three months ago. Context that vanishes when someone leaves. Chaos that scales as the team grows.

The tools that exist either require someone to write things down manually — which never happens consistently — or were built for enterprises with IT departments. Nobody built something for the teams that need it most. EarlyEcho is that solution.

How it works

Set it up once. It runs every day.

01

Tell us how your business works

A two-minute onboarding — business name, what you do, what you don't, team size. That is the only form a client ever fills in manually.

02

Connect your tools

Slack and Gmail connect directly. We also import the last six months of Slack history on day one — so memory starts full, not empty. For every other tool — HubSpot, Notion, Pipedrive, any spreadsheet — clients upload a CSV or Excel export. Native direct connections to project management tools are coming in Phase 2.

03

It reads, learns, and builds memory daily

Every day, EarlyEcho reads new messages, emails, and documents. Facts stated clearly are stored automatically. Anything uncertain or conflicting is held for a one-click human review. The team stays in control of what enters their memory.

04

Ask. Brief. Act.

Query the memory in plain language and get cited answers — every response traces back to its original source. A daily briefing surfaces what matters before the day starts. The agent drafts, prepares, and acts — but only from verified memory, and only when asked.

How accuracy works

Every fact is checked before it enters memory

A dedicated review layer reads every extraction before anything is stored. It checks: is this actually stated in the source? Does it conflict with existing memory? Facts above a confidence threshold are stored automatically. Everything else goes to a one-click human review queue. The system also tests itself regularly against a set of known questions — flagging any drift in accuracy before it reaches the team.

Source message
"gonna push the go-live to the 28th"
Proposed fact
Decision: Launch delayed to 2023-11-28
Review check
Source says "the 28th" only — no month, no year. Date was invented.
Result
⚠ Flagged. Sent to approval queue.
Source message
"dropping Cobalt Retail — margins too thin after scope crept 3 times"
Result
✓ Stored. Decision: Drop Cobalt Retail — thin margins, 3× scope creep.
What's built

Live today and what comes next

Private dedicated environment per client — fully isolated from other organisations Live
Slack connection with full 6-month message history import on day one Live
Gmail connection — reads and ingests email threads automatically Live
Email BCC ingestion — BCC a unique address on any thread and it enters memory Live
Document ingestion — upload PDFs, Word files, and Excel sheets directly Live
CSV and Excel import from any tool — HubSpot, Notion, Pipedrive, spreadsheets Live
Three-tier confidence routing — auto-approve above 80%, human review 50–79%, auto-reject below 50% Live
Approval queue with one-click human review for uncertain facts Live
Automatic contradiction detection — new facts flagged when they conflict with existing memory Live
Chat agent with cited, sourced answers — every response traces back to its origin Live
AI agent — drafts and prepares, acts only on verified memory, only when asked Live
Customisable daily morning briefing Live
Full audit trail — every fact, every approval, every source, every change Live
Self-monitoring — system tests itself against known questions monthly and flags accuracy drift Live
Token usage tracking with monthly budget cap per organisation Live
Native direct integrations with project management tools and CRMs Phase 2
Meeting transcript and voice memo ingestion Phase 2
Self-serve control panel — clients deploy and manage their own environment Phase 2
How we deliver it

Done for you today. Scalable infrastructure tomorrow.

EarlyEcho is being built in two deliberate phases. The first is intentionally personal — we onboard each client directly, configure their environment, connect their tools, and manage the system. This lets us build something that actually works before we scale how we deliver it.

Now — Phase 1

Done for you

  • We onboard each client personally
  • Private environment configured and managed for them
  • First cohort of clients bring their own AI provider credentials — they own the data pipeline end-to-end, we handle everything else
  • Intentionally limited capacity — we are selective about who we onboard
  • Direct relationship with every client
Next — Phase 2

Managed SaaS

  • Self-serve control panel — clients deploy their own environment
  • We manage the AI infrastructure and private server layer
  • Flat monthly pricing with a capped usage allowance included
  • Still single-tenant — every client gets their own dedicated environment
  • Scales to any team size without architectural changes
Pricing

Premium value. Not a per-seat commodity.

Most productivity SaaS charges $20–30 per seat per month and leaves teams to figure out configuration, maintenance, and adoption on their own. EarlyEcho is not that.

EarlyEcho is a managed operating system for a business — deployed, connected, and maintained. The price reflects the outcome delivered, not a seat count. A working company memory that saves hours every week and prevents costly context loss is worth significantly more than another subscription a team has to babysit.

Phase 2 moves to a flat monthly managed tier — predictable, capped, and inclusive of infrastructure. No surprise usage bills. No per-seat math. One price for a system that works.

Who it's for

Starting with small teams. Built to serve anyone.

The future of work is being built by small teams. They are already the fastest-growing segment — lean, fast-moving, operating at a complexity their headcount doesn't suggest. But no tool has been built truly for them. Enterprise tools assume IT departments, long implementation cycles, and dedicated resources most teams simply don't have. That is why we are prioritising them first.

That does not mean we cannot serve larger organisations. The architecture scales to any headcount — a team of 10 and a team of 500 run the same system, the difference is configuration, not capability. We can and will grow with our clients. But we genuinely believe in small teams and what they represent. They are in the most need, being left behind by every tool that defaults to enterprise first, and they are the ones we are building for.

Where we start

Teams of 5 to 50. Agencies, consultancies, early-stage companies, professional service firms — places where one person leaving takes half the company's operating knowledge with them and where no IT team exists to fix it.

Where we grow

Any organisation that has outgrown informal context-sharing. The same single-tenant architecture, the same accuracy guarantees, the same private server — at any headcount, without rebuilding anything.

Why EarlyEcho

What every other tool in this space got wrong.

The capture problem

Search tools can only find what was written down

Notion, Confluence, and Guru all require someone to deliberately write things down. Nobody does this consistently. EarlyEcho captures knowledge from conversations that were never formally documented — decisions in Slack, context in emails, beliefs the team has never put in writing. Then it verifies them before storing.

The knowledge loss problem is not a search problem. It is a capture problem. Nobody was solving that.
The trust problem

AI that writes things down confidently is dangerous

Most AI memory tools store whatever they extract without checking it. EarlyEcho runs every extraction through a dedicated review layer before storing. High-confidence facts go in automatically. Anything uncertain goes to human review. The team always knows what's in there and why.

A memory your team doesn't trust is worse than no memory. Accuracy is not optional.
The isolation problem

Multi-tenant tools share more than teams realise

Most SaaS tools run all customers on shared infrastructure. Client conversations, pricing decisions, and internal processes sit in a multi-tenant database. EarlyEcho gives every client a fully isolated, dedicated private environment. No shared pipelines, no shared databases, no risk of bleed between organisations.

Your company's operating knowledge should not be adjacent to anyone else's. Single-tenant is the only right architecture for this.
The autonomy problem

Teams aren't ready to hand control to autonomous agents

Every AI product today races to claim maximum autonomy. EarlyEcho takes the opposite position. The agent is a secretary — it acts when asked, from verified memory only, and takes on more autonomy only when the team decides they're ready. Trust is built gradually, not assumed.

Autonomy without trust is a liability. We let teams set the pace.
The enterprise gap

Every serious tool was built for companies with IT departments

Glean requires enterprise contracts and dedicated implementation. The tools that do exist for smaller teams are either too lightweight to be serious or too complex to be adopted without a technical owner. EarlyEcho is done for you — no internal technical resource required, no lengthy setup, working from day one.

Small and mid-size teams are not a smaller version of enterprises. They need a different product, not a stripped-down one.
Infrastructure and privacy

A private server that belongs only to you.

Every client runs in a fully isolated, dedicated environment. This is the architectural moat. It means your company's operating knowledge — client names, pricing decisions, internal processes — never sits in a shared database or travels through shared pipelines. The private server is not a feature. It is the foundation the whole product is built on.

Dedicated environment per client — no shared infrastructure, ever
Data fully isolated — never visible or accessible to other organisations
Credentials never passed into the AI — handled at the secure boundary
Full audit trail — every fact, every approval, every source, every change
Token usage capped per organisation — no runaway costs, no surprises

We are onboarding our first cohort now.

EarlyEcho is in early access. We are selective about who we work with at this stage.

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