Pilot
Teams proving the value on one process before committing further.
6 to 8 weeks, fixed scope
- Scoping and success measures
- One production-grade agent
- Pilot report against baseline
- Handover to your team
Agentic AI Services
We design, build, and run AI agents that carry out whole business processes inside your existing systems, with people kept in control of the decisions that matter.
Overview
Most teams have already tried automation. Scripts and RPA handle the clean cases and break on the rest, so people still spend their days reading email, checking systems, and chasing exceptions. Chatbots answer questions but do not finish tasks. Agents close that gap: they work through a task, use your systems to act on it, and hand over to a person when it is outside their remit.
- They take on whole tasks
- An agent plans its own steps toward an outcome, rather than following a fixed script.
- They work in your systems
- ERP, CRM, ticketing, email, and shared drives, with permissions scoped to the job.
- People stay in control
- You decide which actions need approval. Everything the agent does is logged.
What is included
The parts of an agent project we cover, from first scoping to running it in production.
- Agent design and scoping
- We map the process, define what the agent may decide on its own, and set the measures that tell you it is working.
- Systems integration
- Secure connections to your ERP, CRM, ticketing, and document stores through APIs, MCP servers, or RPA where no API exists.
- Knowledge and retrieval
- Agents grounded in your policies, contracts, and past cases, with every answer traceable to its source.
- Multi-agent orchestration
- Specialist agents coordinated across the stages of a longer process, each one tested and explained on its own.
- Human-in-the-loop controls
- Approval rules by value, risk, or confidence, delivered in Slack, Teams, or email so people respond in seconds.
- Evaluation and monitoring
- A test set built from your real cases, run on every change, plus live dashboards for quality, cost, and overrides.
Where it applies
Examples of agents we build, and the part of each job that stays with your people.
| Area | Agent | What it handles | Stays with people |
|---|---|---|---|
| Finance | Invoice exceptions | Matches invoices to POs and contracts, drafts supplier queries, posts clean lines. | Stays with people: Approve exceptions and payments. |
| Customer service | Case resolution | Reads the ticket, pulls order and policy data, drafts or sends the reply. | Stays with people: Refunds above a limit and sensitive cases. |
| Operations | Shipment exceptions | Tracks delays, proposes rebooking, notifies customers and suppliers. | Stays with people: Changes that carry cost. |
| IT | Service desk | Triages requests, resolves standard fixes, prepares access requests. | Stays with people: Privileged access and outages. |
| Sales | Quote and RFP drafting | Assembles first drafts from product documents and past responses. | Stays with people: Pricing and commitments. |
| Risk and compliance | Policy review | Checks documents against policy and flags gaps with citations. | Stays with people: The final determination. |
Approach
A phased delivery model. You see working software and a clear decision point at the end of each phase.
Phase 1
2 weeks
Scope
- Shadow the people who do the work today
- Review about fifty real cases
- Choose the first process and define success measures
- Agree what the agent may and may not do
You receive
Scope document, success measures, and a controls outline.
Phase 2
3 to 4 weeks
Build
- Build the agent and connect it to your systems in a sandbox
- Create the test set from your own cases
- Review progress with your team every week
You receive
A working agent, test results, and the integration design.
Phase 3
2 weeks
Pilot
- Run live on a narrow slice with approvals switched on
- Compare against the baseline you set in phase 1
- Record every override and why
You receive
Pilot report against baseline and a go or no-go recommendation.
Phase 4
Ongoing
Scale and run
- Widen scope and loosen approvals as the evidence supports it
- Monitor quality, cost, and drift
- Tune monthly and add the next process
You receive
Production agents, runbook, monitoring dashboard, and a monthly review.
Engagement models
Most clients start with a pilot. You can move between models as your needs change.
Build and scale
Teams with a successful pilot who want to cover more processes.
3 to 9 months, dedicated squad
- Several agents on a shared platform
- Governance and approval framework
- Integration library for your systems
- Training for your engineers
Managed run
Teams with agents in production who want them looked after.
Monthly, ongoing
- Monitoring and incident response
- Drift and cost management
- Monthly tuning
- Quarterly review with your leadership
Security and tools
How we keep agents safe to run in a real business, and what we build them with.
How we keep control
- Scoped permissions
- Each agent can only reach the systems and actions its job needs. Access starts read-only.
- Approval rules
- Actions above a value, risk, or confidence threshold wait for a person. You set the thresholds.
- Full audit trail
- Every action is recorded with its timestamp, inputs, and sources, and can be exported for audit.
- Data handling
- Agents run in your cloud account or ours. We use models under enterprise terms that do not train on your data.
- Tested before release
- No agent goes live until it passes the test set built from your own cases.
What we build with
- Models
- OpenAI, Anthropic, Google Gemini, and open-weight models
- Orchestration
- AI SDK, LangGraph, Microsoft Agent Framework, or custom
- Integration
- REST and GraphQL APIs, MCP, ERP and CRM connectors, RPA where needed
- Data and retrieval
- Postgres with pgvector, Snowflake, Databricks, Azure AI Search
- Observability
- OpenTelemetry, Langfuse, custom evaluation harnesses
- Cloud
- AWS, Microsoft Azure, Google Cloud, Vercel
We choose the stack to fit yours, not the other way around.
Frequently asked questions
How is this different from RPA or workflow automation?
RPA follows a fixed script and stops when something unexpected appears. An agent works out the steps toward a goal, so it can handle variation, and it knows when to stop and ask. In practice we often use both: RPA for rigid steps, an agent for the judgment around them.
Will the agent make mistakes?
Sometimes, as people do. That is why every agent is tested on your real cases before release, shows its sources, and escalates when it is not confident. In the pilot we measure its error rate against your current process.
Do we need to replace any of our systems?
No. Agents work through the tools you already use.
How do you measure success?
We agree the measures in phase 1, usually handling time, accuracy, and the share of cases completed without a person. The pilot report compares them to your baseline.
What will it cost to run?
We estimate the running cost per task before we build, and track it live afterwards, so the bill is never a surprise.
Who owns the agents after the project?
You do. The code, prompts, and test sets are handed over. Many clients keep us on for managed run, but it is optional.
Not sure which process to start with? Bring two or three candidates and we will help you choose.
Book a discovery callRelated services in Artificial Intelligence
- AI Engineering & DevelopmentCustom models, LLM integration, and production-grade MLOps.
- AI Strategy & ConsultingUse-case discovery, roadmapping, and ROI assessment.
- Support & MaintenanceMonitoring, model-drift management, continuous improvement.
- Upskilling & EnablementTraining and AI adoption programs for your teams.
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