At a glance

Enterprise AI agents that your security team can actually sign off on

Enterprise AI agents

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Your code. Your infrastructure. Production guardrails. Built around your workflow. One accountable engineering team.

What makes an agent enterprise-grade

What it is

Four things separate a production agent from a prototype. It runs with the caller's permissions, not a shared super-user. It records what it did and why. It stops and asks when an action cannot be undone. And someone gets alerted when it fails or drifts.

We design those four in from the first sprint. Bolting governance on afterwards means redoing the data access layer, which is the most expensive part of the build.

We work with mid-market and growth-stage companies that have real compliance needs. We do not claim capacity for programmes that need hundreds of engineers, and we say so on the first call.

Is this the right fit?

Fit check

Where this wins

Regulated or audited environments

Where every action needs a record and access is scoped by role, for example aerospace, healthcare and finance teams.

Internal agents over sensitive company data

Agents that answer questions and produce reports from your database while each person only sees what they are cleared for.

Customer-facing agents with a support cost problem

Agents that resolve most requests alone and hand the rest to a person with full context.

Regulated or audited environments Where every action needs a record and access is scoped by role, for example aerospace, healthcare and finance teams.
Internal agents over sensitive company data Agents that answer questions and produce reports from your database while each person only sees what they are cleared for.
Customer-facing agents with a support cost problem Agents that resolve most requests alone and hand the rest to a person with full context.

When NOT to use this

You want a chatbot over public documents. A simpler build or a chatbot page fits better and costs less.
Nobody in the company owns the agent's outcome. Ungoverned agents cause more trouble than the manual work they replace.
You need a certified compliance programme delivered. We build to your controls, we do not issue certifications.

How an enterprise agent build runs

How it works

Narrow first. One agent, one job, one owner, then widen access as it earns trust.

Typical timeline

Typically six to ten weeks to a governed pilot, scoped after the audit call

Stack we build with

Claude · OpenAI · MCP · PostgreSQL · Supabase · TypeScript · Python · n8n

Scope and risk review

Name the job, the owner, the data it touches and the actions that must never run unattended.

Access and tool design

Define per-role scopes, the tools the agent may call and the approval rules for each.

Build and evaluate

Build against sandbox or sample data, with a test set of real questions and known-bad inputs.

Pilot with a small group

A small group uses it on real work while we watch logs, failures and cost.

Operate

Monitoring, alerting, model and dependency updates, and monthly review of failures.

01 Scope and risk review Week 1 Name the job, the owner, the data it touches and the actions that must never run unattended. Deliverable One-page agent charter with risk register
02 Access and tool design Week 1 to 2 Define per-role scopes, the tools the agent may call and the approval rules for each. Deliverable Access model and tool specification
03 Build and evaluate Week 2 to 6 Build against sandbox or sample data, with a test set of real questions and known-bad inputs. Deliverable Working agent with an evaluation suite
04 Pilot with a small group Week 6 to 8 A small group uses it on real work while we watch logs, failures and cost. Deliverable Pilot report and fixes
05 Operate Monitoring, alerting, model and dependency updates, and monthly review of failures. Deliverable Runbook and maintenance plan

Where enterprise agents earn their keep

Use cases

Customer Support Automation

Legal & Compliance Automation

Saved €10K in Fines

Marketing Operations Automation

Ask your own database

Non-technical staff query company data and get reports and charts limited to what they may access.

Multilingual support agent

Handles the bulk of inbound requests around the clock and escalates the rest with context.

Sales research agent

Prepares accounts, updates the CRM and scores leads before a call.

What you have after 30 days

First 30 days

The controls exist before the agent does anything important.

Agent charter

Week 1

The job, owner, data sources and actions that always require approval, written down and agreed.

Access model

Week 2

Who sees what, enforced in the data layer rather than in a prompt.

Working agent on sandbox data

Week 3 to 4

Running against sample data with logging on and an evaluation set in place.

Pilot plan

Day 30

Named pilot users, success measures and the alerting that will watch it.

Scope and exclusions

Scope

In scope

Agent architecture, tool design and the access model
Integrations with your existing systems and identity provider
Approval steps, audit logging and evaluation tests
Monitoring, alerting and a maintenance plan

Not in scope

Security certifications or attestations for your company
Training or hosting your own foundation model
Replacing your identity or data platform
Agents that act on money or safety-critical systems without a human step

Results from comparable work

Results

Achieved results only. Clients that have not agreed to be named are described instead.

The whole database became an agent anyone can talk to. Each person gets their own scope and only sees and generates reports and charts for what they are allowed to touch.

A multilingual WhatsApp AI agent now handles 80% of requests without a human touching them. Response time had averaged over 4 hours. The client reports roughly $18,000 a year saved and support workload down 40%.

Agents do pre-call research, update the CRM and score leads automatically. Research time per prospect dropped and the sales team's conversion rate went up.

How this is priced

Pricing

Priced per project and scoped after a short discovery call, not sold as a fixed package. Cost follows the number of systems, the access model and the security requirements.

Scoped build

Scoped after a discovery call

Most engagements start with one narrow, high-value piece so you see it running in production before anything expands.

Embedded engineer

From $60,000/year

A dedicated engineer building and maintaining the work inside your team, instead of a scoped project.

Describe the agent and the data it would touch

A 30 minute call. We look at the job, the data and the controls you need, and tell you what a governed first version would take.

In this call, we'll walk through your project scope, timeline, and goals - so we can both check if we're a fit. No obligation, no slide deck, just a working session.

Consultation

This call is for teams ready to move. If that's you, pick a time.

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Before we build

At a glance questions

Practical answers about scope, delivery, ownership, and working with ManasFlow.

How do you keep an agent from seeing data it should not?

Access is enforced in the data layer, using the caller's identity and role, not in the prompt. A prompt can be talked around. A query the database refuses cannot.

Can the agent take actions, or only answer questions?

Both, with a rule per action. Reversible, low-risk actions can run unattended. Anything irreversible, or involving money, waits for a person to approve.

Do you host the agent or do we?

Either. Many teams keep it in their own cloud account so data stays inside their boundary. We design to whichever hosting model your security team approves.

What does it cost?

Priced per project and scoped after a short architecture audit. Cost follows the number of systems, the access model and your security requirements. Most engagements begin with one narrow agent so you see measured results before expanding.

Who maintains it after launch?

We can, under a maintenance plan covering monitoring, model updates and monthly failure review. Or we hand it to your engineers with a runbook.