Skip to content

Pillar 01

AI Engineering & Intelligent Systems

AI chatbots, knowledge assistants, AI agents and AI features in your product, built around one defined workflow and tested against agreed criteria.

When to involve us
When you want to apply AI to a specific product or workflow, or take an AI pilot into reliable production use.
Usually for
CTO, product and operations leaders

What does AI Engineering cover?

AI Engineering & Intelligent Systems is the Directrix Technologies practice for applying AI to a defined business workflow and taking it into dependable production. It covers AI chatbots and virtual assistants, enterprise knowledge assistants built on retrieval-augmented generation (RAG), task-oriented AI agents, evaluation and guardrails, model selection, and AI productionisation. Every engagement starts from one workflow and an agreed way of measuring success, and consequential outputs keep a named human reviewer. Directrix projects in this area include DocuMind, Knowledge Navigator, CineAssist and SmartFind.

Recommended starting pointPilot / project

AI chatbot and virtual assistant

Implement a website, in-app or internal assistant for a defined set of user intents and approved knowledge.

What you get

  • Conversation flow
  • Knowledge integration
  • Escalation path
  • Analytics
  • Test cases

Sounds familiar?

Users wait for answers to repetitive questions or cannot navigate information.

Typical uses

Website FAQs, onboarding assistant, internal help desk.

Usually for

Customer experience, product, support

Good to know: Not a substitute for professional medical, legal or financial advice.

Discuss AI chatbot and virtual assistant
Recommended starting pointPilot / project

Enterprise knowledge assistant (RAG)

Build a search-and-answer experience grounded in approved company documents and knowledge sources.

What you get

  • Document ingestion
  • Retrieval pipeline
  • Answers with source links where supported
  • Access controls
  • Evaluation set

Sounds familiar?

Employees or customers spend time searching fragmented documents and FAQs.

Typical uses

Internal policy assistant, product-support knowledge base, operations handbook.

Usually for

IT, customer support, HR, operations

Good to know: Answer quality depends on source quality, permissions and evaluation.

Discuss Enterprise knowledge assistant (RAG)
Recommended starting pointPilot / project

Task-oriented AI agents

Build bounded AI workflows that use approved tools and explicit checkpoints to complete multi-step tasks.

What you get

  • Tool integrations
  • State and permission controls
  • Retry/timeout handling
  • Audit logs
  • Tests

Sounds familiar?

Teams need help with repetitive, multi-step work across software tools.

Typical uses

Ticket enrichment, lead research, document routing.

Usually for

Operations, product, engineering

Good to know: Autonomy should be limited to a defined risk level and human approval points.

Discuss Task-oriented AI agents
Recommended starting pointProject

AI productionisation and deployment

Move a validated AI feature into a monitored production environment with defined operational controls.

What you get

  • Deployment pipeline
  • Logging/metrics
  • Rollback plan
  • Operational runbook

Sounds familiar?

A demo works but lacks reliability, monitoring, security or release procedures.

Typical uses

RAG systems, model APIs, AI workflows.

Usually for

Engineering, platform, product

Discuss AI productionisation and deployment

More AI Engineering services

  • AI strategy and use-case discovery

    Identify where AI can improve a workflow, product or customer experience; prioritise by value, feasibility, risk and data readiness.

    You get: Prioritised use-case backlog · Feasibility assessment · Success metrics · Recommended next step

    Good to know: Recommendations depend on access to stakeholders and representative data.

    Discuss this

  • AI solution architecture

    Design the application, model, data, security, evaluation and deployment components for a defined AI use case.

    You get: Architecture diagram · Component choices · Data flows · Risk register · Rollout plan

    Discuss this

  • Document-grounded search

    Create semantic and keyword search across documents, product data or internal records.

    You get: Indexing pipeline · Search interface/API · Ranking configuration · Relevance tests

    Discuss this

  • LLM workflow orchestration

    Connect prompts, model calls, retrieval, business rules and external tools into controlled workflows.

    You get: Orchestration workflow · Error paths · Observability hooks · Runbook

    Discuss this

  • AI evaluation and regression testing

    Measure answer quality, task completion, grounding, consistency and failure patterns on representative test cases.

    You get: Golden test set · Evaluation metrics · Regression report · Release gates

    Discuss this

  • AI guardrails and human review

    Add input/output checks, policy rules, escalation paths and reviewer queues around AI features.

    You get: Risk controls · Moderation/review flow · Test scenarios · Escalation rules

    Good to know: Controls reduce risk; they do not guarantee zero errors or legal compliance.

    Discuss this

  • Model selection and benchmarking

    Compare suitable hosted or open models on quality, latency, privacy, deployment constraints and cost.

    You get: Evaluation plan · Benchmark results · Trade-off matrix · Recommendation

    Discuss this

  • AI gateway and model routing

    Centralise model access, routing, retries, quotas and usage visibility across applications.

    You get: Gateway configuration · Routing rules · Keys and access policy · Usage dashboard

    Discuss this

  • LLM cost and latency optimisation

    Analyse usage patterns and optimise model choice, caching, context size, batching and retrieval.

    You get: Baseline · Optimisation backlog · Before/after test · Monitoring plan

    Good to know: Savings depend on the current baseline, workload and traffic pattern.

    Discuss this

  • Prompt and context engineering

    Develop and test structured prompts, task instructions, context assembly and output schemas.

    You get: Prompt library · Schema validation · Tests · Versioning approach

    Discuss this

  • Structured extraction and classification

    Extract fields or classify content into controlled schemas with confidence checks and exception handling.

    You get: Extraction/classification pipeline · Schemas · Validation rules · Exception queue

    Discuss this

  • Recommendation and personalisation systems

    Combine business rules and suitable ML/AI methods to personalise recommendations or user journeys.

    You get: Recommendation logic · Data inputs · Evaluation plan · Integration API

    Good to know: Recommendations require agreed criteria and permissioned data.

    Discuss this

  • Predictive analytics and applied ML

    Assess and implement predictive models for a defined business target where data supports it.

    You get: Data readiness review · Baseline · Trained model if feasible · Performance report

    Good to know: Model performance depends on data quality and target stability.

    Discuss this

  • AI-powered summarisation and reporting

    Generate structured summaries or draft reports from approved records with traceability and review options.

    You get: Summary workflow · Report template · Source references where available · Review step

    Discuss this

  • Multimodal AI integration

    Combine text, image, audio or video processing in a workflow when technically appropriate.

    You get: Processing pipeline · Supported input/output formats · Evaluation examples · Integration

    Good to know: Supported formats and model capabilities must be confirmed during discovery.

    Discuss this

  • AI prototype and proof of concept

    Build a time-boxed prototype to test a high-priority use case before a production commitment.

    You get: Working prototype · Test results · Known limitations · Production recommendation

    Good to know: Prototype scope and production readiness are separate decisions.

    Discuss this
  • Generative AI & AI Agents
    Sample demoFeatured

    CineAssist

    AI-Powered Customer Support

    CineGear Pro, London, UK

    A conversational AI assistant that answers routine customer questions in natural language.

    • Generative AI
    • Customer Service Automation
  • Generative AI & AI Agents
    Sample demoFeatured

    DocuMind

    Enterprise Document Intelligence

    Enterprise AI project

    Make large document collections searchable and answerable in natural language using RAG.

    • Generative AI
    • RAG
    • Document Processing
  • Generative AI & AI Agents
    Featured

    Knowledge Navigator

    Private, Offline AI Search

    University of East London, UK

    AI-assisted document search that runs in a controlled local environment, without sending documents to the cloud.

    • Private AI
    • Retrieval-Augmented Generation
    • Enterprise Search
  • Generative AI & AI Agents
    Sample demo

    SmartFind

    Intelligent Product Search & Recommendations

    CineGear Pro, London, UK

    Semantic product search and recommendations that match meaning, not just keywords.

    • Artificial Intelligence
    • E-Commerce
    • Semantic Search
  • Machine Learning & Data Intelligence

    FingerCheck 2.0

    Biometric Recognition & Attendance

    University of East London, UK

    Fingerprint recognition using Siamese neural networks, integrated into an attendance application.

    • Computer Vision
    • Biometrics
    • Attendance Automation
  • Machine Learning & Data Intelligence

    WorkforceIQ

    AI-Powered HR Analytics

    Employee attrition prediction, workforce segmentation and interactive analytics dashboards.

    • Machine Learning
    • Predictive Analytics
    • Business Intelligence
Next pillarCustom Software & Digital Products
CallWhatsApp