RAG Knowledge Assistant for Internal Teams
RAG assistant trained on contracts, SOPs, policies, and project history with citations and access control.
Production AI systems
AI Engineering Consulting
Production AI agents, RAG systems, document intelligence, LLM applications, evaluation, guardrails, and integration — built to work in real operations, not just demos.
What we build
Typical engagements
Production-grade AI software with real business logic, integrations, and engineering maturity behind it.
AI Business Agents — Autonomous agents that execute multi-step business processes: support triage, claims processing, back-office operations, and internal workflow automation.
RAG Knowledge Assistants — Retrieval-augmented generation systems with citations and role-based access. Query contracts, manuals, reports, and knowledge bases in plain language.
Document Intelligence — Classification, extraction, validation, and routing of invoices, contracts, forms, and clinical records — replacing slow manual data-entry pipelines.
LLM Product Features — AI capabilities embedded inside existing SaaS and enterprise software through a controlled integration and architecture layer.
Evaluation & Guardrails — Evaluation harnesses, model routing, cost control, and guardrail design for AI systems already in production or preparing for it.
Private-Cloud & On-Premise AI — Sensitive-data workflows deployed on private cloud or on-premise infrastructure with appropriate access controls and audit logging.
Architecture
How production AI is structured
A production AI system combines deterministic software and probabilistic models. Both layers need to be designed, tested, and monitored correctly.
01
Application layer
Controls identity, authorisation, business rules, data access, audit logging, and actions. Deterministic — no AI guessing here.
02
AI layer
Handles language understanding, generation, extraction, ranking, and summarisation. Probabilistic — needs measurement and monitoring.
03
Retrieval & context
RAG pipelines, embeddings, chunking, re-ranking, and context management for grounded, accurate, and citable outputs.
04
Evaluation harness
Representative test cases, acceptance thresholds, failure tracking, and regression testing before every model or prompt change.
05
Observability
Latency, cost, accuracy, error rates, and drift monitoring. Alerting on business-logic failures, not only infrastructure failures.
06
Human-in-the-loop
Confidence thresholds, escalation paths, approval queues, and fallback rules — so the system knows when to ask, not just when to act.
Discovery
What a useful discovery produces
Before writing a line of production code, a proper AI engineering engagement defines scope, constraints, and success criteria precisely enough to evaluate the result — and to stop a project that shouldn't proceed.
Start with a discovery callBounded workflow
A clearly defined business process and measurable target outcome.
Data inventory
Access constraints, sensitivity classification, and retention rules documented before architecture decisions.
Evaluation dataset
Representative test cases and acceptance thresholds agreed before build begins.
Delivery roadmap
Architecture options, cost drivers, integration approach, and operating model in one clear document.
Evidence from related work
Case studies
Production AI systems delivered for clients in insurance, healthcare, professional services, and enterprise operations.
AI Claims Intake Agent
LLM-based intake assistant that reads emails, extracts claim data, checks policy context, and drafts structured summaries for human review.
AI Document Processing for Healthcare
Secure processing of medical records, lab files, and referral forms with RAG search, schema-based extraction, and validation rules.
Enterprise Workflow Automation Hub
CRM, email, documents, and approval flows connected into a unified automation layer with AI-generated task summaries.
Ready to build AI that works in production?
Book a discovery call with an AI engineering expert. We'll map your use case, define the architecture, and scope a controlled pilot — no overcommitment on day one.
Questions buyers ask
Frequently asked questions
What is included in AI engineering consulting?
It can include use-case discovery, data assessment, architecture, model selection, RAG, agent workflows, evaluation, security, integration, deployment, observability, and optimization.
How do you choose an AI model?
Model choice should be based on task quality, data sensitivity, latency, cost, context requirements, deployment constraints, and evaluation results rather than brand preference.
What is the role of deterministic software?
Deterministic software controls permissions, business rules, state changes, validation, audit logs, and transactions. The AI component should not silently bypass these controls.
How is an AI system evaluated?
Use representative test cases, expected outcomes, failure categories, accuracy or task-success metrics, latency, cost, safety checks, and human review of ambiguous cases.
Can AI be added to an existing product?
Yes. The safest approach usually starts with a bounded workflow and an integration layer rather than rebuilding the full product around AI.