SolutionAI Assistants

AI & Automation

Assistants and copilots grounded in your data, with explicit guardrails

Production assistants use approved corpora and APIs, emit structured logs, and define escalation paths when outputs fall below confidence or policy thresholds.

Use-case design focused on measurable handle time or resolution improvements.

Retrieval, prompt, and evaluation loops tuned to your risk tolerance.

Cost and latency controls so production usage stays predictable.

On this page

Overview

Whether internal helpdesks or customer-facing copilots, we connect models to vetted knowledge and APIs - not the open web by default.

We collaborate with security and legal on data handling, retention, and human-in-the-loop patterns where regulations apply.

Core services

Components we combine and sequence based on your constraints and timeline.

Use-case & policy

Scope, disallowed topics, PII handling, and success metrics.

Knowledge & tools

Connectors to docs, tickets, or APIs; tool calling with limits.

Evaluation

Test sets, regression checks, and dashboards for quality and drift.

Deployment

Hosting choices, secrets, monitoring, and operator documentation.

Typical flow

A reference sequence; we adapt depth and gates to your organisation.

#StageWhat happens
01Pilot

Feasibility slice

Narrow scenario on a curated corpus to validate accuracy and UX.
02Harden

Safety & scale

Guardrails, rate limits, logging, and red-team style testing.
03Launch

Controlled rollout

Phased users, feedback capture, and support workflows.
04Improve

Continuous tuning

Retraining on new content, eval updates, and cost reviews.

Who we work with

Support teams, professional services, and product companies adding AI features - from SMEs piloting copilots to enterprises with model governance boards.

Infrastructure

We integrate with major model providers and can run in your cloud tenancy (AWS, Azure, GCP) with private networking as required.

Deliverables

Concrete outputs, documented and handed over with the build.

  • Use-case definition and guardrails
  • Connection to your knowledge base or APIs
  • Evaluation and monitoring basics
  • Operator documentation

Engagement model

Partnership patterns we document in the SOW or master agreement.

  • -Proof of concept before full production
  • -Collaboration with your security and legal teams as required

Commercial model

Cost reflects retrieval architecture, evaluation depth, risk controls, and model usage (often pass-through). We quote after discovery.

We start with a focused discovery (paid or unpaid, depending on complexity). You receive a written scope or SOW: milestones, acceptance tests, and a defined change process. NDAs and your procurement steps are routine.

Fixed scope

Documented requirements, milestones, and acceptance criteria. Delivery targets an agreed release or go-live.

When it applies

A narrow use case, curated corpus, and human-in-the-loop before broader rollout.

Phased programme

Successive increments with checkpoints, integrations, and change control as scope evolves.

When it applies

Production guardrails, logging, multiple tools or data domains, and security or legal review cycles.

Ongoing partnership

Retained monthly capacity for maintenance, incremental features, releases, and operational support.

When it applies

Monitoring, prompt and eval updates, and iteration as usage and content change.

Fees are quoted per engagement after discovery. Third-party cloud, licensing, and usage charges are usually billed to your accounts unless we agree otherwise.

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