AI & Data Engineering
Put AI into production — not into a slide deck
Most AI projects stall between the demo and the deployment. We build the unglamorous parts that make the difference: evaluated prompts, retrieval that returns the right document, guardrails, cost controls, observability and a data platform that can actually feed a model. Then we ship it behind your existing auth, on your existing cloud, with a rollback plan.
- A working system in production, not a prototype
- Measured accuracy with a regression suite you own
- Predictable token and infrastructure cost per transaction
- Full audit trail of every model call
Capabilities
What this practice actually covers
Written at the level of detail you would need to decide whether we can do the specific thing you have in mind.
LLM applications & copilots
Domain assistants that sit inside the tools your team already uses — support desks, ERPs, internal portals, field apps — grounded in your own content and permissions.
- Retrieval-augmented generation (RAG) over documents, tickets, code and databases
- Structured output and tool/function calling wired to your real APIs
- Streaming chat, summarisation, drafting and classification workflows
- Prompt versioning, A/B evaluation and golden-set regression testing
Agentic automation
Multi-step agents that complete real back-office work — reconciliations, document intake, procurement checks, QA triage — with a human approval step wherever the stakes justify it.
- Tool-using agents with scoped permissions and audit logging
- Model Context Protocol (MCP) servers exposing your internal systems safely
- Deterministic orchestration with retries, budgets and circuit breakers
- Human-in-the-loop review queues and escalation paths
Document & vision intelligence
Turning the paper-shaped parts of a business into structured data: invoices, KYC packs, purchase orders, lab reports, engineering drawings and site photography.
- OCR and layout-aware extraction into validated schemas
- Classification, redaction and PII detection
- Computer vision for inspection, counting and defect detection
- Confidence scoring with automatic fallback to manual review
Classical ML & forecasting
When a gradient-boosted tree beats a language model — demand planning, churn, pricing, credit scoring, anomaly detection — we say so and build that instead.
- Feature engineering and feature stores
- Time-series forecasting and demand planning
- Recommendation and ranking systems
- Drift monitoring and scheduled retraining
Data platform & engineering
The layer everything else depends on. Reliable pipelines, a modelled warehouse, and governance that survives an audit.
- Ingestion and ELT pipelines, batch and streaming
- Dimensional modelling in a lakehouse or warehouse
- Data quality tests, lineage and cataloguing
- Vector stores, embeddings pipelines and hybrid search
MLOps & AI governance
Everything needed to run models responsibly once real users depend on them.
- CI/CD for prompts, models and datasets
- Evaluation harnesses and offline/online metrics
- Cost, latency and token telemetry per feature
- Access control, data-residency and retention policy enforcement
In detail
AI readiness — a four-week assessment
Before writing production code we run a fixed-scope assessment so you know what is worth building, what it will cost, and what it will return. You keep the output whether or not you continue with us.
Week 1 — Opportunity mapping
Interviews across operations, support and finance to inventory candidate workflows, then score each on value, feasibility and risk.
Week 2 — Data & systems audit
What data exists, where it lives, how clean it is, and what integration surface your systems actually expose.
Week 3 — Technical spike
A narrow, throwaway prototype against your real data to prove or kill the top-ranked use case before budget is committed.
Week 4 — Roadmap & business case
Sequenced delivery plan, architecture, build-vs-buy calls, cost model, governance requirements and a go/no-go recommendation.
Deliverables
What you receive
- Use-case assessment with a scored, sequenced roadmap
- Working production system with source code and IaC
- Evaluation suite and accuracy baseline you can re-run
- Cost model per user, per transaction and per month
- Runbook, monitoring dashboards and alerting
- Team enablement sessions and handover documentation
Typical stack
Technologies we reach for
- Claude
- OpenAI
- Llama
- LangGraph
- MCP
- Python
- PyTorch
- pgvector
- Pinecone
- dbt
- Airflow
- Snowflake
- Databricks
Chosen per engagement rather than by default. See the full technology stack for what we work with across practices.
Questions
About ai & data engineering
Do we need to send our data to a third-party model provider?
Not necessarily. We design for three deployment postures: hosted frontier models with zero-retention agreements, models running inside your own cloud tenancy, or fully self-hosted open-weight models on your infrastructure. The choice is driven by your data-residency and compliance requirements, and we document the trade-off in cost and capability for each.
How do you stop the model from making things up?
Grounding, constraints and measurement. Answers are retrieved from your sources with citations rather than generated from model memory, outputs are constrained to validated schemas where structure matters, and every change is scored against a golden dataset before it ships. Where a wrong answer is expensive, we route low-confidence cases to a human.
What if AI is not the right answer for our problem?
We will tell you during the assessment, and often we do. A rules engine, a better report or a fixed integration frequently beats a model on cost, latency and reliability. Recommending against a build is a normal outcome of the four-week engagement.
Can you work with the AI features we have already started?
Yes. A common engagement is taking an internal proof-of-concept and doing the productionisation work — evaluation, guardrails, cost control, security review, observability and deployment — rather than starting over.
Other practices
Related capabilities
Have a ai & data engineering problem?
Send a short description of the problem. You will get a reply from an engineer, not a sales sequence — usually with a first read on the approach and whether we are the right partner for it.
Or email us directly at info@beambytes.com