What you have now
- Nine subscriptions, nine logins, nine bills
- Nothing talks; people are the glue
- No single view of the business
- You adapt to the tool, not the reverse
AI Operating System
One system to run your operations, not a vendor stack.
Your team lives in a dozen disconnected tools and copies data between them all day. We put sales, ops, marketing, finance, and support on one AI operating system: objects, agents, and an audit trail on a single model of how your company runs. Start in chat where your team already works, then add the deeper modules. It is the system we run Zentarai on, branded as yours.
Governance, around all of it
Immutable audit log on every read, write, and AI action. Row-level access via SSO.
The problem
A dozen tools, none of them talking.
Every subscription solves one slice and sees one slice. Phone in one place, deals in another, documents in a third, scheduling in a fourth, the CRM you pay for sitting empty because logging into a separate tool never sticks. Between them sits a person copying data from one screen to the next.
Paying for nine tools that do not talk
Every subscription solves one slice. Nothing sees the whole picture, so you pay nine bills for a business you still cannot see in one place.
People paid to move data
Hand-built reports, status updates, and a chase to see if anyone followed up. Your team spends hours being the glue between systems that should talk on their own.
You cannot see your own company
Ask how you are doing right now and the answer takes six apps and a day. By the time you see a problem, it has already cost you.
The architecture
Three layers. One model of your operations.
Connectors feed an ontology of your business. Agents act on that ontology. Governance wraps all three. The same shape large institutions pay seven figures for, deployed faster and priced for you.
Objects, relationships, events
Customers, deals, properties, transactions, calls. Each becomes queryable, relationships are first-class, and every state change is a timestamped event.
Agentic workflows
Agents read the ontology, take actions, and log every decision with a full reasoning trace. A collections agent, a support agent, a planning agent, all on one model.
Connectors, pipelines, governance
Pulls from your CRM, ERP, ticketing, calls, and docs. PII flagged on ingest, lineage tracked end to end, the audit trail immutable.
Governance, around all of it
Immutable audit log on every read, write, and AI action. Row-level access via SSO.
The digital twin
Four parts, one company brain.
Underneath the four parts is one thing: a living digital twin of your organization. The company brain holds every person, project, promise, and event, connected in one place and kept current as work happens. A Normalizer keeps the twin in sync with reality, the Brain reasons and acts on it, a Predictor simulates what is likely next, and a Console keeps a person in control. Select a part to see what it does.
In plain terms
A factory keeps a live model of its machines so it can watch, test, and tune them without stopping the line. The runtime keeps that same kind of model of how your company runs: a digital twin of your operations that stays current as work happens, so you can do four things with it.
See itOne current picture of who did what, when, and what was promised.
Ask itAnswers in plain language, with full context and cited sources.
Test itSimulate the impact of a slip or a change before it lands.
Act on itDraft, schedule, and follow up, with a person approving what matters.
Company brainYour live digital twin
The Brain
Understands, decides, acts
The front door. A request arrives, the Brain reads what is being asked, routes it to the right specialist, then drafts and acts on the result.
- Ten action-taking agents, from research and drafting to scheduling, lead follow-up, and forecasting. Add your own without waiting on engineering.
- A roster of advisory skills (Chief of Staff, BizDev, Finance, Email QA) that think and write. Author a new one from a short brief.
- The company brain: a knowledge base with passage-level retrieval and a three-dimensional graph of how every fact connects.
- A decision debugger that shows exactly how the Brain reached its choice.
The loop, signal to learned outcome
- 01Signal in
A message, email, commit, or alert arrives through a connected channel or webhook.
- 02Clean it up
The Normalizer ties it to the right person and project, adds it to the timeline, and pulls out any promise.
- 03Understand it
The Brain answers with full context from knowledge, the timeline, and open commitments, with sources.
- 04Look ahead
Models forecast what is likely next: the impact of a slip, an unusual spike, a risk score you defined.
- 05Act
Agents draft, schedule, and follow up, pausing for human approval when the action carries weight.
- 06Record and learn
Every action and prediction is logged and graded, then feeds the next round.
Step six feeds back into step one, so the same record powers answers, forecasts, and actions, and each round makes the next one sharper.
Connections and channels
Activity flows in from chat, email, and code platforms; tools act out to create issues, send mail, and update records. A new connection is a setting, not a code change.
Configuration and white-label
One profile describes the company. Swap the AI, the storage, or the sign-in underneath. Ship it blank for a new customer or pre-loaded for day one.
Trust and security
Human approvals on risky actions, a full audit trail, masked personal data, encrypted credentials, and role-based access through your identity system.
What ships on the runtime
The runtime, in working parts.
Everything below runs on the same ontology, the same audit trail, and the same identity layer.
Visual ontology designer
Define objects, properties, and relationships in a UI. Schemas are version-controlled and reversible, so a model change is a commit, not a migration project.
Ten-plus native integrations
Salesforce, HubSpot, NetSuite, Oracle, custom databases, Slack, Teams, Twilio, Resend. A new connector is one to two sprints, not a quarter.
Natural language and SQL
Ask the model anything across your ontology. It generates the query with provenance attached, so every answer comes with the rows that produced it.
Workflow agents
Long-running agents watch objects, fire on events, and run multi-step workflows. Every action lands in the audit log with its reasoning trace.
Audit and access control
An immutable log on every read, write, and AI action. Row-level access through SAML or OIDC single sign-on, mapped to the roles you already run.
Compliance, built in
PII auto-redacted in logs. Data residency in your region or ours. Built to align with SOC 2 controls, with finance and privacy frameworks designed in rather than bolted on.
One operating system, every department
Pick the modules you need. They share one brain.
One data layer, one assistant, one login. Every department finally runs on the same model, with the same audit trail and identity layer.
Sales
Pipeline, lead capture, instant follow-up, and a board that updates itself. Nothing falls through the gaps between calls.
Operations
Tasks, checklists, and workflows that route themselves. The system chases the work so your managers do not have to.
Marketing
Content, campaigns, and social in one place, drafted by the assistant and queued against your calendar.
Finance
Cash, invoices, and runway in real time. The numbers move as the business moves, not at month-end close.
Support
Every customer message in one inbox, with replies drafted for you and the ones that need a human flagged.
Assistant and autopilot
An assistant that reads the whole company and does the tasks, and an autopilot that runs the routine ones without being asked.
What runs on the runtime.
The operating system is the base. These modules deploy on top of it, drop-in, on any existing stack.
Document digitization and understanding: every contract, receipt, claim, or filing becomes a queryable object, with OCR, extraction, and a full audit trail.
An inbound and outbound voice agent that answers calls, books appointments, and recovers missed ones, plugged into your CRM through the runtime.
Cross-channel orchestration: Slack, email, SMS, voice, and the web app unify into one customer object, so agents see the whole picture and route accordingly.
Early-warning and collections workflows that read the same ontology, share the same audit trail, and run on the same identity layer as everything else.
Where your team works
Start in chat. That is the whole interface.
No new app, no training week. The same runtime shows up in the channel your team already opens forty times a day. A handful of plain commands they will know inside five minutes, then add the deeper modules when you are ready.
deal: Acme Corp, sent proposalLogged, timestamped, searchable.
remind me in 3d follow up with SarahSet in seconds, fires in your channel.
briefOpen deals, follow-ups due, blockers.
what did we quote TechFlow?Searches your history, answers in plain English.
todo add review contractLogged. React to close it.
#sales
8:00 AMYour sales OS, already in chat
What the runtime does in your channel, every day.
No dashboards to check. The same runtime works in the surface your team already opens forty times a day.
Morning brief at 8am
Open deals, follow-ups due today, blocked tasks, and yesterday's end-of-day updates. Delivered in your channel before the first coffee, every day.
One-line deal logging
Type a deal in a sentence and it is logged, timestamped, and searchable. No second tool to keep up to date, so the record actually stays current.
Reminders that fire
Set a follow-up in a sentence and it arrives in your channel on the day. No calendar invite, no missed follow-up two weeks later.
A company brain
Ask what you quoted, what you agreed, who owns what. It searches your shared history and surfaces the answer in seconds instead of forty minutes of scrolling.
A nightly learning loop
Each night it reads the day's activity, extracts new context, and updates what it knows. The runtime gets sharper over time without anyone maintaining it.
End-of-day check-ins
A short prompt at 5pm: done, blocked, next. The answers feed tomorrow's brief automatically, so nobody has to chase a status update.
Where it sits
Too big for spreadsheets. Too smart for enterprise bloat.
There is a gap between duct-taped subscriptions and a $200K custom build. The AI Operating System is that middle.
The right-sized middle
The fit- One system, one login, one bill
- An assistant that does the busywork
- The whole company, in real time
- Built to your business, live in weeks
- White-label, from $7.5K plus $1.5K a month
Overkill for most
- $200K-plus and 9 to 18 months
- Consultants, not operators
- Heavy to change once shipped
- Built for the Fortune 500, not you
Priced to scale with you.
$7,500+ $1,500/mo
One-time build plus monthly. No long contract.
Your highest-pain modules, built and branded as yours.
The assistant and autopilot included from day one.
Hosting, updates, and new modules in the monthly.
Live in weeks. Start small, add modules as you grow.
Multi-location or multi-department: from $25K build plus $3K a month, scaling by modules and seats. The enterprise tier on the same runtime adds a visual ontology designer, data residency in your region, and bank-grade compliance built to align with SOC 2 controls. Final scope and price are confirmed on your demo call.
The honest numbers.
Modeled on your data before you sign, not signed-customer results. We show the math on your own bills and workflows first.
- ~60%
- Typical cut to the SaaS line once tools collapse onto one runtime, modeled on your bills
- Weeks
- From kickoff to a configured runtime live in your workspace, not a 9-month build
- 10+
- Native connectors, with custom ones in one to two sprints
- 100%
- Of reads, writes, and AI actions written to an immutable log
See the AI Operating System on your own data.
We listen first, then show you exactly which of your departments, objects, and workflows run best on the runtime. Twenty minutes, no slides.