Agents that interpret, decide and execute across your enterprise systems — CRM, ERP, ticketing, email, and custom backends. Bounded reasoning infrastructure. Audit trail generated by default. Human-in-the-loop validation checkpoints integrated automatically on any action that is irreversible. The concrete productivity gain that previous AI implementations continually promised but never delivered.
We do not ship unsupervised autopilots. We ship digital operators engineered to pass rigorous regulatory sign-off.
Four briefs we hear repeatedly — and how we address them. If one of these reads like your week, the conversation starts with a process map, not a slide deck.
"Operations is drowning in coordination work."
Categorising enquiries, routing tickets, chasing status, stitching information across tools. The marginal cost of the ten-thousandth enquiry is the same as the first. Agentic loops compress that work into a background process.
"The intelligence lives in one senior operator's head."
Processes that officially take hours actually take days, because the unwritten rules sit with one person. We capture the SOP, encode the gates, and the agent ships the senior operator’s judgement at scale.
"Previous AI efforts stalled at the recommendation layer."
The model suggested; a human still picked up the mouse. The productivity gain never arrived. Agentic systems close that gap — the agent finishes the action, with retries, fallback and a record.
"Compliance will not sign off on autonomous action."
No lineage, no explainability, no reversibility. We design for audit on day two, not week twenty. Every agent has an auditor’s chair at the design table.
The agentic stack we ship to production. Every capability runs through the Planner / Router / Executor / Validator loop — so the lineage, the evaluation set and the human-in-the-loop gates are the same regardless of which capability you start with.
Agents operate directly within your core CRM, ERP, email, calendars, ticketing, ITSM, HRMS, and bespoke backends. This is not a detached sidebar interface — it integrates directly into your existing operational architecture.
The structural Planner / Router / Executor / Validator framework. Delivering bounded reasoning infrastructure, transactional retries with graceful system fallbacks, and a comprehensive execution trace an auditor can review end-to-end.
Every automated decision is tethered to verifiable evidence. Native source citation is active by design; ungrounded generation modes are completely disabled. The agent explicitly states its knowledge parameters and its exact data source.
Mandatory validation gates applied to irreversible transactions or regulator-sensitive procedures. The agent escalates anomalies with full contextual data preserved — replacing blind handoffs with a traceable trail of operational breadcrumbs.
Every system decision logs a clear logical rationale, a verified source, and an unalterable audit trail. The structured compliance pack is generated as an automated byproduct of the runtime, never compiled after the fact.
Hardcoded domain rules, operational red-lines, and conditional escalation triggers. The agent is structurally blocked from violating regulatory guardrails — because compliance boundaries are locked in the underlying codebase, not left to chance in a prompt.
“The LLM is merely a single component. The core execution loop is the actual product. The immutable audit trail is the final deliverable.”
Talk to an engineerA working summary of what we ship into production today. We do not chase the leaderboard; we choose the model that holds up to the eval set and the regulator. Filter by layer to see what fits your stack.
Frontier APIs, open-weight models, agent frameworks — chosen for the eval set, not the demo.
The contracts the auditor will read. Event-sourced where it earns its keep.
Design-system-aligned consoles for the humans in the loop — not chat windows for everyone else.
Run in your cloud or ours — sovereign, hybrid or private. The architecture follows the regulation.
Outcomes measured the way the auditor measures them — on the eval set, after human verification, with the cost and latency we agreed at the start. Not the demo number. The real one.
Ranges reflect outcomes across recent engagements. Per-engagement targets are agreed in writing during Discover.
The AI & Automation practice ships through three connected workstreams — embedded intelligence inside existing software, generative-AI systems built around your corpus, and the data architecture that keeps both trustworthy at scale.
The agent lives inside the operational workflow, not in a separate chat window. Surfaced where the work happens.
Read more / 02LLMs used for understanding and synthesis; tool calls used for action. Grounded, cited, governed.
Read more / 03Event-sourced logs, replayable decisions, feedback loops into evaluation suites. The plumbing the agent depends on.
Read moreA summary of the briefs we are running this year. Click through for the case studies and the eval-set numbers behind each row.
Why agentic AI, and why with us.
Agentic systems fail in ways conversational chat demos never expose—transactional retries loop endlessly, critical APIs time out, and operational policies shift mid-flight. Having shipped thousands of systems, our engineering teams know exactly where the architectural breaking points are and how to mitigate them.
Every automation agent is built with a compliance framework integrated by week two, not week twenty. The comprehensive, regulator-ready audit pack is an automated architectural byproduct of our runtime, never a presentation deck compiled after deployment.
The principal architect who scopes your technical requirements is the exact engineer who deploys your production system. We eliminate the traditional consulting pyramid, removing any translation layer between strategic advisory and technical execution.
When autonomous agents must read, classify, or extract unstructured document data to complete an enterprise task, Actigen is natively embedded in the workflow—delivering a unified automation platform instead of forcing seven distinct vendor integrations.
Every prompt iteration, data retrieval, downstream API call, and validation checkpoint is automatically written to a standardised audit pack recognised by regulatory bodies. This transparency is not an add-on requested after the fact; it is active by default.
Our project delivery is bound by strict engineering governance, not optimistic timelines. Every production release ships complete with updated operational runbooks, clear system SLOs, and the precise compliance evidence packs your next internal reviewer will demand by name.
Engagements where the loop has been measured, the lineage retained, and the auditor has seen the evidence pack. Filter by service or industry to widen the view.
CASE 01
US university research initiative
260 years of fragmented historical maps transformed into a georeferenced spatial database for anthropological and land-use analysis. A prominent US university needed to study the historical evolution of Uxeau, France, across multiple centuries of land ownership, taxation, and agricultural activity. The research depended on digitising and harmonising vintage maps dating back to 1759 — each with different scales, formats, and levels of degradation — into a single spatially accurate GIS environment suitable for comparative analysis. 260+ years of historical mapping digitised and layered. Lambert II precision georeferencing using Esri GIS tools. Multi-era land parcel and feature extraction delivered at scale.
30,000+ CT scans annotated at 98.9% segmentation accuracy for AI-driven radiology models. A US-based MedTech AI company developing radiology models for tumour detection and analysis required clinically precise annotation support to accelerate model training and validation. Existing workflows faced rising costs, limited access to qualified medical annotators and growing compliance pressure around handling sensitive patient imaging data.
40%+ faster foreclosure data processing with 50% higher accuracy across multi-county property records. A US-based mortgage data company managing property intelligence across more than 155 million properties and 3,000+ counties required a scalable operational model for foreclosure data collection and processing. Their existing workflows relied heavily on manual back-office operations, creating delays, inconsistencies and rising operational overhead across fragmented government data sources.
The commercial shape changes; the governance does not. Whichever model you pick, you get the same audit pack, the same SLOs and the same named accountability.
A standing pod — engineers, ML, ops — embedded with you, owning the agent programme end-to-end.
A specific agent, shipped to a defined outcome. A fixed price. A date the auditor can hold us to.
Senior AI engineers attached to your team — under your delivery model, to our standard.
Agentic AI goes wrong when treated as a model problem. It is an operations, policy and systems-integration problem in which the model is one component. Our delivery reflects that.
Process mapping, policy capture, value hypothesis, risk surface. One conversation, not a questionnaire.
Agent topology, tool inventory, guardrails, evaluation harness, human-in-the-loop gates — all named on the page within the working week.
Iterative, with a red-team track running alongside the green-team track. Evaluation gates per release.
Observability, cost monitoring, policy updates, model refreshes. The 99.1% on-time figure is enforced here.
“The same loop runs every brief. The only thing that changes is which gate matters most.”
Contact us“We asked for an agent that could close a ticket without supervision. We got one our compliance team now uses as the reference example for how autonomous action should be governed — rationale on every step, lineage on every decision, and a kill-switch the auditor knows the name of.”Head of Operations UK general insurer — Agentic AI — claims triage
Not a sales call. Not a qualification form. A representative process map, a clear question, and a measured pilot proposal returned within the working week.
We use essential cookies to make this site work, and optional cookies to understand how you use it. You can accept all, reject non-essential, or choose what to allow.
