Agentic AI Implementation
Agents that do the work,
not just talk about it
An AI agent is software that pursues a goal: it reads, decides, calls your systems and completes tasks — with human approval where it matters. We design, deploy and operate agents in your public cloud or entirely inside your own infrastructure.
Use cases
Work agents take off your team's plate
Ticket & email triage
Agents read incoming requests, classify them, gather the missing context from your systems, and either resolve or route them with a summary attached.
Document processing
Invoices, contracts, CVs, claims: extraction, validation against your rules, and entry into your ERP or CRM — with every decision logged and reviewable.
Workflow orchestration
Multi-step processes that used to need three people and a checklist: onboarding, reporting, reconciliation — executed end-to-end with approval gates you define.
Research & monitoring
Agents that watch sources you care about — tenders, prices, regulations, competitors — and deliver structured briefings instead of raw noise.
Internal knowledge answers
Grounded question-answering over your documentation, policies and past decisions — with citations, so people can verify instead of trust blindly.
Operations copilots
Agents embedded in your team's tools that draft, summarize, check and prepare — keeping a human in the loop for every consequential action.
Deployment
Public cloud or on-premises — an honest comparison
Both are first-class options for us. The right choice depends on your data sensitivity, existing infrastructure and budget. Here is how we lay out the trade-off with clients:
| Public cloud | On-premises | |
|---|---|---|
| Where your data lives | Processed by the cloud AI provider under contract; stored in your cloud tenant. | Never leaves your hardware. Full stop. |
| Model training on your data | Disabled via enterprise agreements and API settings — contractual guarantee. | Architecturally impossible. The model runs locally; there is no channel for your data to reach a training pipeline. |
| Best for | Fast starts, variable workloads, teams already in AWS / Azure / GCP. | Regulated industries, sensitive IP, strict data-residency requirements. |
| Model capability | Frontier models, always current. | Strong open-weight models, selected and tuned for your specific tasks. |
| Upfront cost | Low — pay per use. | Higher — GPU hardware or a dedicated server, sized by us to the real workload. |
| Running cost at scale | Grows with usage. | Flat and predictable once deployed. |
The on-premises guarantee, in plain words
With an on-prem deployment, the AI model runs on machines you own or rent, inside your network. Your prompts, documents and results are never transmitted to a third party and never used to train any AI model — ours or anyone else's. Your competitive knowledge stays exactly that: yours.
Delivery
From idea to operating agent
Agents fail when they're launched as demos and abandoned. Ours are delivered as operated systems: measured, supervised, and improved on the same kaizen loop as everything else we run.
Discuss an agent project- 01
Discovery & task selection
We pick one process with clear value and measurable outcomes — not "AI everywhere".
- 02
Pilot in weeks
A working agent on real data, with approval gates, run alongside your current process.
- 03
Hardening & integration
Access controls, audit logging, fallback paths, and connections to your live systems.
- 04
Operate & improve
We monitor quality and cost, review edge cases monthly, and expand scope only when the numbers justify it.
In practice
What agentic AI looks like in a real business
Three engagement profiles that show where agents pay for themselves fastest.
Logistics · invoice intake
The supplier mailbox agent
An agent reads incoming supplier invoices, matches them against purchase orders in the ERP, posts the clean ones and routes exceptions to a human with the discrepancy highlighted.
Manual entry largely eliminated; exceptions surfaced the same day instead of at month-end.
Construction · on-premises
The tender scout that keeps bids private
A fully on-prem agent scans procurement portals nightly, scores opportunities against the company's criteria and drafts a first-pass summary — with bid strategy never leaving the building.
No missed deadlines; a ranked shortlist waiting every morning, on hardware the client owns.
Insurance · claims triage
First-pass claims assessment
An agent extracts details from incoming claims, checks completeness against policy rules, requests missing documents automatically and queues prepared files for the adjuster's decision.
Adjusters start from prepared files instead of raw email threads; approval gates keep humans in charge.
Representative engagement profiles — happy to walk you through the specifics on a call.
Have a process an agent should own?
Bring us the workflow that eats your team's hours. We'll tell you honestly whether an agent can do it — and prove it with a pilot.
Start the conversation