Agents engineered to the operating problem.
Off-the-shelf agents force the process to fit the tool. The engagement model inverts that constraint: we begin from your operating friction, scope a deliverable, architect the agent against your knowledge surface and systems, and ship it to production. Self-serve is not on offer here — applied engineering is.
Why generic AI agents fail in production environments.
Off-the-shelf AI tools optimise for the median customer, not yours. They lack access to your private knowledge, your transaction systems and your operational rules — and they have no incentive to integrate. The result is a parallel layer of automation that creates fresh silos rather than dissolving the existing ones.
Skilled labour absorbed by routine
Domain experts spending their week on data entry, document triage and reconciliation — work that is uniform, high-volume and structurally automatable.
Disconnected data substrate
CRM, ERP, ticketing and the documentary record live in separate stores. Cross-system reasoning is exactly the gap a generic agent cannot bridge without explicit integration.
Absence of operating context
Hallucination is a context problem before it is a model problem. An agent without grounded access to your private data, policies and historical outcomes will fabricate confidently — and at scale.
An engagement, not a self-serve product.
There is no drag-and-drop builder in this offer. Our engineers diagnose the friction, define the agent's perimeter of authority, design the retrieval and tool surface, and ship the agent into your environment. The deliverable is a production system, not a prototype handed back over a wall.
Grounded in your operating reality
Each agent is wired into your private knowledge surface via Agentic RAG: documentation, contracts, transactional databases, historical decisions. Retrieval is auditable, answers are cited at the source, and out-of-scope queries are refused by design rather than fabricated.
Deterministic tool surface
The agent reasons, but it also acts. Tool invocations carry explicit contracts and are persisted for review:
- Parse, extract from and synthesise long-form documents.
- Initiate messaging actions in Slack, WhatsApp or Microsoft Teams.
- Read from and write to CRM (HubSpot, Salesforce) and ERP, under defined authority.
- Trigger multi-step business logic through n8n workflow orchestration.
Designed against organizational reality
Drawing on organizational psychology, each engagement examines the handoff geometry — where the agent acts autonomously, where it escalates, where it defers. The objective is sustained adoption, not a successful demo.
Three classes of engagement we run repeatedly.
Most engagements fall into one of three architectures. The scoping conversation establishes which architecture the operating friction warrants.
Document and data intelligence
Agents that parse 500-page contracts to surface risk clauses, or analyse financial filings for material anomalies. Output is structured, cited and audit-ready.
Multi-agent orchestration
Composed pipelines: a research agent gathers, an analyst agent reconciles and reasons, a writer agent synthesises. Each step's input and output are inspectable in turn — scoped per engagement; not all features require this architecture.
Custom predictive modelling
Where the organization has historical data with sufficient signal, we scope bespoke predictive modelling — churn risk, pricing posture, sentiment classification — to complement rather than replicate LLM reasoning. Each is engaged case by case.
Integrates with your stack
Real-time notifications, user sync, and BI-ready data. One platform, every tool you already use.
Real-time Notifications
Slack
Microsoft Teams
Google Chat
User Provisioning & Sync
SCIM 2.0
Microsoft Entra ID
Okta
Google Workspace
Advanced BI & Reporting
GraphQL
Microsoft Excel
Power BI
Google Sheets
Tableau
HR Software Sync
BambooHR
Personio
HiBob
Factorial
Workday
The agent runs where your data lives.
Data residency, sovereignty and compliance constraints determine where the agent is deployed — we adapt to them, never around them.
GDPR-compatible, privacy by design. Sensitive business data remains within the perimeter you nominate — managed, private cloud or on-premise.
Managed cloud
We operate the infrastructure, monitoring and patching on your behalf. Availability and response SLAs are written into the engagement contract — not advertised generically.
Private cloud or on-premise
Deployment into your AWS, Azure or GCP tenancy — or onto on-premise infrastructure. Data never crosses your defined network perimeter.
Integration into existing surfaces
The agent surfaces where work already happens — web console, Slack channel, Teams thread, or as a headless API that other systems consume.
From operating friction to production system, in four phases.
Diagnosis and scoping
We surface the operating friction, define the agent's perimeter of authority, identify the knowledge surface and tool integrations required. Output: a scoped specification.
Engineering build
Our engineers implement the reasoning logic, select and configure the underlying models, and wire the retrieval index and tool contracts within an isolated environment.
Validation and red-teaming
Before production traffic, the agent is exercised against a representative set of real prompts from the engagement scope. Failure modes — hallucination, scope drift, tool misuse — are surfaced manually and corrected against explicit acceptance criteria.
Production and continuous tuning
The agent enters production. Performance and quality metrics are monitored under an ongoing engagement; the agent is tuned as the operating context evolves.
Open a scoping conversation.
Bring the operating friction you would otherwise resign yourself to. We assess feasibility, define the deliverable and respond on scope, timeline and economics.
A working conversation with engineers, not a sales demo.
