Autonomous AI Agents · Inside your perimeter

AI agent development services. Agents that act, not just answer.

Agents that plan a goal, use your tools, and carry the work through to done — completing the task, updating your systems, closing the loop. Deployed inside your environment, with scoped permissions and a human at every gate that matters.

Speak with our team →See how an agent works
The gap

A chatbot answers. Your work needs something that acts.

A copilot drafts a reply and hands the work back to you. Real throughput comes from agents that take the next step themselves — across your systems, end to end. But to act, an agent needs real access, and that's exactly why a public agent platform is a non-starter for a serious enterprise: it demands broad reach into your tools and sends your data somewhere you don't govern. We build agents that act inside your perimeter, with permissions you scope and approvals you control.

How an agent works

A loop, not a reply. Plan, act, check, repeat.

  1. 01

    Perceive

    Reads the state of your systems — tickets, documents, events, queues — to understand what needs doing.

  2. 02

    Plan

    Breaks the goal into steps and decides which tools and data it needs to get there.

  3. 03

    Act

    Executes through your APIs and tools — creates, updates, ships — using only the permissions you grant.

  4. 04

    Verify & report

    Checks its own work, logs every action, and escalates to a human at the gates you set.

Put them to work

The same loop, pointed at real work.

Customer support

Resolve tickets end-to-end against your knowledge base and systems, handing off to a person on edge cases.

Finance & operations

Reconcile invoices, chase exceptions, and prepare reports across your internal tools.

Sales & RevOps

Enrich leads, keep the CRM clean, and draft follow-ups grounded in real account history.

IT & DevOps

Respond to alerts, run runbooks, open incidents, and file the postmortem — around the clock.

Research & analysis

Gather, synthesise, and cite from your own data and public sources, on demand or on a schedule.

What we build

Agentic AI development, end to end.

From the first workflow worth automating to a fleet of agents in production, we design, build, secure and run the whole thing with your team.

Agent strategy & discovery

We find the workflows where an agent pays back fastest, and the actions where it should never act alone.

Custom single-workflow agents

One agent, one job, done end to end: ticket resolution, reconciliation, alert triage, research.

Multi-agent systems

Planner, worker and reviewer agents that split a complex process and check each other’s output.

Tool integration over MCP

Your CRM, ERP, ticketing and databases exposed as scoped, audited tools through MCP servers we build.

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Grounded agents with RAG

Agents that read your documents and data through retrieval, and cite exactly what they used.

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Evals & AgentOps

Eval suites, tracing, cost and latency budgets, and a regression gate on every prompt, tool or model change.

Control & governance

Autonomy, with a hand on the brakes.

An agent that can act needs guardrails an auditor would sign off on. Every agent we build runs on least-privilege access, pauses for human approval on the actions you flag, executes in a sandbox, and writes a complete log of what it did and why — so the autonomy stays inside the lines of GDPR, HIPAA, DPDP, and whatever else your sector demands.

  • Sandboxed execution
  • Scoped permissions
  • PII redaction
  • Human-in-the-loop
  • Action audit log
  • Rollback
Where it runs

Private AI agents, self-hosted inside your VPC.

To act, an agent needs real access to your systems. That access should never leave your perimeter. We deploy agents in your cloud account, or fully on-prem and air-gapped on the same private LLM infrastructure we run for regulated teams.

AttributePublic agent platformBNCW agent in your VPCOn-prem or air-gapped
Where it runsThe vendor’s cloudYour cloud accountYour data centre, no internet egress
Where your data goesThe vendor’s serversStays in your tenantNever leaves your building
ModelsThe vendor’s choiceOpen-weight on your GPUs, or private endpointsOpen-weight on your hardware
Tool accessBroad OAuth grantsScoped per tool and per actionScoped per tool and per action
Approvals & auditVendor-definedYour gates, full action logYour gates, full action log
Built onA proprietary runtimeOpen standards: MCP, LangGraph, open weightsOpen standards: MCP, LangGraph, open weights
Under the hood

Production agents. Not a weekend demo.

Durable orchestration that survives restarts, tools wired in over the Model Context Protocol, open-weight models on your own GPUs, evaluated and fully observable — the same stack we run in production, deployed entirely within your tenant.

Your VPC
  • Your APIs & toolswhat the agent acts on
  • MCPtool protocol
  • LangGraphplanning graph
  • Temporaldurable execution
  • vLLMopen-weight models, your GPUs
  • pgvectormemory & retrieval
How we build an agent

Five steps. Read-only until it earns write.

01

Discover

Map the workflow, the systems it touches and the actions that need a human. Pick the agent with the shortest path to value.

02

Prototype

A working agent on your real data and tools, read-only first, with an eval set that defines what correct means. Live in 2–3 weeks.

03

Harden

Approval gates, scoped credentials, sandboxing, injection tests and failure handling. Production-ready in 6–10 weeks.

04

Roll out

Shadow mode, then supervised, then autonomous on the actions you approve. Your team sees every step in the audit log.

05

Run

Monitoring, evals on every change, and new tools as the workflow grows. We operate it, or hand it over clean.

Engagement

What an AI agent costs. Priced in the open.

Fixed price, agreed before we write a line of code. Start with a pilot, prove the value on your own data, then scale what works.

Pilot

from $7,500
2–3 weeks

One workflow, one agent, read-only first, running on your real data and tools.

  • →Up to 5 tools
  • →Runs in your cloud or ours
  • →Eval report you can show your board
Most common

Production

from $25,000
6–10 weeks

A live agent with write actions, approval gates and full hardening, inside your VPC.

  • →Scoped credentials per tool
  • →Approval gates & audit trail
  • →Injection-tested before launch
  • →Runs in your VPC

Enterprise

custom
3–6 months

Multi-agent systems and rollouts across teams, through your security review.

  • →Planner, worker & reviewer agents
  • →On-prem or air-gapped if required
  • →Security & compliance review support

AgentOps

from $3,000/mo
ongoing

We run the agent after launch, so it keeps getting better instead of drifting.

  • →Monitoring & alerting
  • →Evals on every change
  • →Tuning & new tools monthly
What you get

Capacity that does the work.

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Work gets done, not just drafted

Agents complete multi-step tasks end-to-end — not a suggestion you still have to action yourself.

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You stay in control

Approval gates, scoped access, and a full audit trail of every action the agent takes.

→

Runs in your environment

Agents act on your systems from inside your perimeter — not from a vendor’s cloud with god-mode access.

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Scales without headcount

Run one agent or a fleet. Added capacity is infrastructure, not another hire.

Questions

The things enterprises ask first.

Aren’t autonomous agents risky to let loose on real systems?

Only if they’re ungoverned. We build agents with scoped permissions, human-in-the-loop approval gates on sensitive actions, sandboxed execution, and a full audit log — so every action is bounded, reviewable, and reversible.

How is an agent different from a chatbot or copilot?

A chatbot or copilot answers and hands the work back to you. An agent takes the next step itself — it plans a goal, calls your tools, and completes the multi-step task, checking its work along the way. The difference is action, not conversation.

How much does AI agent development cost?

A pilot starts at $7,500: one workflow, read-only, live on your data in two to three weeks. A production agent with write actions, approval gates and hardening starts at $25,000. Multi-agent and enterprise rollouts are scoped custom, and AgentOps support after launch starts at $3,000 a month. Prices are fixed and agreed before we start.

How long does it take to build an AI agent?

A read-only pilot is live in two to three weeks. A production agent with integrations, approval gates and security hardening typically takes six to ten weeks. Multi-agent systems and company-wide rollouts run three to six months, mostly driven by how many systems are involved and how strict the compliance review is.

How is an AI agent different from RPA?

RPA replays fixed clicks and breaks when a screen or a format changes. An agent reasons about the goal, reads unstructured input like emails and PDFs, chooses which tool to call, and checks the result. We often keep your existing bots and let the agent decide when to run them.

What can agents connect to?

Your APIs and internal tools — CRMs, ERPs, ticketing systems, Slack, databases, and document stores — via connectors and the Model Context Protocol. The agent only ever gets the access you explicitly grant.

Can AI agents work with our legacy systems?

Yes. Anything with an API, a database or a file drop can become a scoped tool. We wrap older systems in MCP servers, and where there is no API at all, the agent works through an existing RPA bot or a controlled export.

How do you stop an agent from taking an unsafe action?

In layers. The agent only sees allow-listed tools, write actions need scoped credentials, high-impact actions pause for a named approver, everything runs in a sandbox, and every call is logged. Before launch we test the agent against prompt-injection and tool-misuse suites.

Which frameworks and models do you use?

LangGraph for agent logic, Temporal for durable long-running work, MCP for tools, and pgvector for memory. Models are chosen per task: open-weight models such as Llama or Mistral served with vLLM on your GPUs, or frontier models through private endpoints such as AWS Bedrock when your policy allows.

Do agents run continuously or on demand?

Both. Agents can run long-running and durable — triggered by events, schedules, or queues for round-the-clock work — or on demand for a specific task. State and progress survive restarts.

Who maintains the agent after launch?

We do, or your team does. Either way it ships with monitoring, runbooks, and an eval suite that runs on every prompt, tool or model change. We operate it under a support plan, or hand it over clean with documentation and training.

Should we build our own AI agent instead?

If you have engineers with production LLM experience, you can, and we’re happy to review your design. Teams bring us in when the agent needs write access to core systems, has to pass a security review, or must run on private infrastructure. That is the part that takes longest to get right.

Put an agent to work.

Tell us a workflow that eats your team's time — and we'll scope an agent that runs it, inside your environment, with the guardrails you need.

Talk to our team →