Skip to main content

Service

AI & Data Engineering

We build practical AI — retrieval-augmented assistants, document extraction, forecasting and BI — grounded in your own data with evaluation, guardrails and human oversight.

70%
Manual effort removed
94%
Extraction accuracy
Faster reporting cycles
24/7
Autonomous support cover

Capabilities

What this practice actually covers

LLM copilots, document intelligence and analytics pipelines that turn scattered operational data into decisions.

01

RAG Copilots & Assistants

Assistants grounded in your policies, SOPs and product data with citations, access control and refusal behaviour you can audit.

02

Document Intelligence

Invoice, PO, KYC and claim extraction combining OCR with LLM reasoning and a human-in-the-loop review queue.

03

Predictive Analytics

Demand forecasting, churn scoring, credit risk and anomaly detection wired directly into operational systems.

04

Data Platform Engineering

Lakehouse and warehouse builds with orchestrated ELT, lineage, contracts and data-quality monitoring.

05

BI & Decision Dashboards

Power BI, Metabase and embedded analytics with governed semantic layers instead of duelling spreadsheets.

06

Voice & Conversational AI

Speech-to-text, intent routing and multilingual voice bots — including the engine behind our IVR product.

Technology

The stack we build this on

Chosen for long-term supportability, hiring depth and operational maturity rather than novelty.

Models

ClaudeGPT-4 classLlamaWhisperSentence Transformers

AI Tooling

LangChainLlamaIndexpgvectorPineconeQdrantRagas

Data

AirflowdbtKafkaSparkSnowflakeBigQuery

ML Ops

MLflowWeights & BiasesVertex AISageMaker

Visualisation

Power BIMetabaseSupersetRecharts

Delivery process

Six phases, no black boxes

You will know exactly where the project stands at any point, and what happens next.

01

Use-Case Triage

Score candidate use cases on value, data readiness and risk — we say no to the ones AI cannot honestly solve.

02

Data Readiness

Source audit, quality profiling, PII classification and the ingestion plan that makes everything downstream possible.

03

Prototype & Evaluate

A working prototype with a labelled evaluation set and accuracy thresholds agreed before production is discussed.

04

Guardrails

Prompt-injection defence, PII redaction, grounding checks, refusal policy and full interaction logging.

05

Integrate

Embed into CRM, ERP, helpdesk or portal so the output lands where work actually happens.

06

Monitor & Improve

Drift detection, feedback capture and scheduled re-evaluation as models and data evolve.

Architecture & design patterns

The patterns we apply, and why

Architecture decisions are recorded in a decision log you keep — readable, challengeable, and still useful long after we have handed over.

Retrieval-Augmented Generation

Answers grounded in your indexed corpus with citations, so the model reasons over facts instead of recalling them.

Human-in-the-Loop

Confidence thresholds route uncertain cases to a reviewer; every correction becomes training signal.

Medallion Data Architecture

Bronze, silver and gold layers give raw fidelity, cleaned entities and business-ready marts.

Event-Driven Ingestion

Change-data-capture streams keep the AI layer fresh without nightly batch lag.

Evaluation-Driven Development

Golden datasets and regression evals gate every prompt or model change, exactly like unit tests.

Semantic Layer

One governed definition of each metric so every dashboard agrees on what revenue means.

Deliverables

Exactly what you receive

Not a vague promise of 'the software'. Every engagement lists its artefacts up front, and handover is not complete until each one is signed off.

  • AI opportunity assessment & scorecard
  • Data quality and lineage report
  • Evaluation harness with golden dataset
  • Production RAG / model service
  • Guardrail and safety policy document
  • Dashboards and semantic model
  • Drift monitoring alerts
  • Team enablement workshop

FAQ

Questions about ai & data engineering

No. We deploy through enterprise endpoints with zero-retention terms, or fully self-hosted open models where data residency demands it. Your corpus stays inside your boundary.

Free consultation

Need ai & data engineering?

Send us the brief — or just the problem. We respond with an approach, an indicative timeline and a costed range within three working days.