An AI workforce that does the work, not the demo.
AnovaCloud designs, builds, integrates, and governs teams of AI workers — ten production role blueprints that plug into your systems, follow your policies, and run your workflows end to end. Humans approve exceptions. Workers handle the volume.
Ten workers. One operating model.
Each worker is a production role blueprint: defined inputs, tool access, memory, quality checks, and escalation rules. We configure the role to your systems and your data — we don't ship a generic assistant and hope.
Research Worker
Deep-dive research across internal documents, the web, and structured sources — with citations for every claim. Produces briefing packs, competitive scans, and diligence memos your analysts can trust because the sources are attached.
IN → question + source scope → OUT cited brief, source listCustomer Support Worker
Resolves tickets against your knowledge base, order systems, and policy docs. Drafts replies in your tone, executes refunds or account actions within policy limits, and escalates what it can't verify — with the full trail attached.
IN → ticket + KB + policy → OUT resolution draft or executed actionData Analyst Worker
Answers ad-hoc questions against your warehouse with generated SQL, sanity-checked numbers, and charts. It knows your metric definitions and refuses to hallucinate joins — ambiguity goes back to the requester with options.
IN → question + semantic model → OUT verified answer + SQLOperations Worker
Runs recurring operational processes: intake triage, scheduling, status chasing, vendor follow-ups, document checks. It keeps checklists, nudges owners, and reports exceptions — the work that currently dies in inboxes.
IN → process checklist → OUT completed steps + exception logKnowledge Worker
Maintains your institutional memory: ingests wikis, tickets, and transcripts; curates the knowledge base; flags stale docs; and answers "how do we do X?" with the current, approved procedure. The antidote to tribal knowledge.
IN → docs + tickets + transcripts → OUT curated KB, freshness reportsEngineering Worker
Code review, test generation, migration assistance, and repo maintenance inside your SDLC. It opens real PRs against your standards, runs your CI, and never merges without human approval — a tireless junior with senior guardrails.
IN → ticket + repo + standards → OUT PR, tests, review notesSales Worker
Prospect research, CRM hygiene, meeting prep briefs, follow-up drafting, and pipeline data quality. Your reps spend their hours selling; the worker handles everything that happens between the calls.
IN → account + CRM → OUT brief, enriched record, draft follow-upFinance Worker
Invoice processing, expense validation, reconciliation drafts, and variance commentary. Every number traces to a source document; nothing posts to the ledger without a human's sign-off — automation with an audit trail.
IN → invoices + policies → OUT validated entries + variance notesIT Support Worker
Level-1/2 service desk: password resets, access provisioning, device troubleshooting, ticket routing. Resolves repetitive requests directly, documents what it did, and hands real problems to your engineers with context.
IN → ticket + runbooks → OUT resolution or enriched escalationDatabase Worker
Monitors your databases, explains slow queries, drafts optimized rewrites, validates backup and replication health, and prepares migration evidence. Your DBAs get leverage; your databases get a 24/7 first responder.
IN → metrics + schema → OUT diagnosis, rewrite proposal, health reportOne blueprint. Five layers.
Every worker runs on the same governed platform. That is what makes ten workers manageable instead of ten science projects.
One front door
Authentication, routing, and rate limits for every request — human chat, API call, or scheduled run. Nothing reaches a worker without an identity and a budget.
Plans, delegates, recovers
The orchestrator breaks a request into steps, assigns them to the right workers, retries failures, and assembles the answer. Multi-step work survives multi-step failure.
Roles, not prompts
Each worker carries its role definition, tool permissions, memory, and quality checks. Swapping the model underneath doesn't change what the worker is allowed to do.
Your systems, safely reached
Warehouse, RAG indexes, SaaS APIs, and databases behind one tool plane with scoped credentials. Workers get exactly the access their role needs — nothing more.
Trust is a layer, not a hope
Policy gates on high-risk actions, eval suites that run on every prompt or tool change, a complete audit trail, and human approval where policy demands it.
Regression-tested agents
Every worker ships with a golden set of test cases. Change the model, the prompt, or a tool, and the suite runs before anything reaches production.
Design. Build. Integrate. Govern.
A worker deployment is an engineering project with a start, a scope, and acceptance criteria — not an experiment that never ends.
Design — workflow first
We map the actual workflow: inputs, decision points, systems, exceptions, and where humans must stay in the loop. We pick the worker role, define its tools and policies, and set the success metric against your current human baseline.
Build — on your data
We configure the worker against your knowledge base, warehouse semantic model, and APIs. Evals are written before tuning begins, so quality is a gate, not a hope. Shadow runs compare worker output to human output on real history.
Integrate — into the flow of work
Workers land where work already happens: Slack, Teams, your ticketing system, your CI pipeline, scheduled jobs. We handle identity, secrets, networking, and the boring plumbing that decides whether agents survive contact with production.
Govern — after go-live
Audit logging, policy gates, eval regression on every change, and a clear escalation path. We hand you the runbook and train your team — or we operate the platform for you under a managed engagement.
Discovery → Pilot → Production
Three phases, each with a decision gate. You can stop after any of them with something useful in hand.
Discovery
Workflow mapping for one high-value process. We score candidate worker roles on value, data readiness, and risk — and you get a deployment blueprint with the metric we'll beat.
Pilot
One worker role, one real workflow, shadow mode against human baselines. Go/no-go is decided by the numbers: accuracy, cycle time, and exception rate — not vibes.
Production
Roll out to the full process, add worker roles in priority order, harden governance, and hand over operations. Most clients sequence 3–5 roles across the first two quarters.
Starting point for most teams: a 2-week AI Readiness Assessment that maps your workflows to the ten worker roles and prices the pilot. Request the assessment →
Questions we hear before every deployment
How is an AI workforce different from a chatbot or copilot?
Chatbots and copilots assist a human in a single session. An AI workforce is a set of persistent workers with defined roles, tool access, memory, and approval policies that execute multi-step business processes end to end — drafting, researching, reconciling, filing — with humans reviewing exceptions and approvals, not every step.
How do you govern AI workers in production?
Every worker runs inside the governed agent platform: identity and least-privilege tool access, policy gates on high-risk actions, a full audit trail of prompts, tool calls, and outputs, and eval suites that run on every change before promotion. Humans approve what policy says humans must approve.
How long does it take to get a first AI worker into production?
A pilot of one worker role on one real workflow typically runs 4–6 weeks: discovery and workflow mapping, building on your data with guardrails, then a shadow-mode pilot measured against human baselines before any production rollout.
Do AI workers replace our team?
In our experience they change what the team spends time on. Workers take the repetitive, rules-bound volume; people keep judgment, exceptions, relationships, and ownership. We design every deployment with a human-in-the-loop model and measurable productivity targets.
What data access do AI workers need?
Only what the role requires. Workers authenticate with their own service identities, inherit least-privilege access to the systems their role touches, and every read and write is logged. We map data lineage for each worker during design, not after deployment.
Can we start with just one role?
Yes — that is the recommended path. Most clients start with the Knowledge Worker or the Customer Support Worker, prove the operating model on one workflow, then expand role by role. The platform is built once; adding roles is configuration, not a new project.
One workflow. One worker. Six weeks.
Talk to an architect about which of the ten worker roles pays for itself first in your organization.