AI integration is the fastest-growing engineering category on the market right now — we build the governed version of it.
Armour AI X

AI platform & integration practice

AI that reaches production — and survives the audit.

Most AI pilots stall because nobody can prove what the model did. We build AI integrations, agents and document pipelines with validation, controls and a full audit trail — the way regulated businesses actually need them.

Book a scoping call See what we build

Deterministic validation layers · human-in-the-loop controls · full lineage

Built on

  • OpenAI & Anthropic APIs
  • MCP
  • Python
  • Azure
  • AWS
  • GCP
  • Snowflake
  • Databricks
  • Vector databases
  • Docker

Services

Everything an AI programme needs — from strategy to the running system.

Twelve service lines covering the fastest-moving areas of AI demand today. Engagements start as a fixed-scope audit or pilot, then scale into build and run.

Most in demand

AI integration

Wiring models into the systems you already run — ERP, CRM, data warehouse, internal tools. APIs, orchestration, retries, cost controls and observability, not a notebook.

  • LLM APIs
  • Orchestration
  • Legacy systems
Premium tier

AI strategy & consulting

Where AI actually pays in your operation, what it costs to run, and which use cases to refuse. Reference architecture, build-vs-buy, and a sequenced roadmap.

  • Roadmaps
  • Costing
  • Build vs buy

Chatbots & AI agents

Assistants that can actually do things — tool calling, permission-aware access, escalation paths, and evaluation so behaviour is measured rather than hoped for.

  • Tool calling
  • RBAC
  • Evals

Document AI & data extraction

Turning statements, invoices, contracts and forms into validated structured records that land in your system of record with an audit trail behind every field.

  • OCR + LLM
  • Validation
  • ERP write-back

RAG & knowledge platforms

MCP-native knowledge layers that give assistants governed access to your documents, databases and internal tools — so answers cite sources instead of inventing them.

  • MCP
  • Vector search
  • Citations

AI video generation & editing

Production pipelines for generated and edited video at volume — brief to render to review, with brand rules enforced and versions tracked, not one-off prompt runs.

  • Batch pipelines
  • Brand rules
  • Review flow

AI image generation & editing

Image workflows wired into your asset pipeline: templated generation, variant cascades, background and retouch automation, with consistent output at scale.

  • Variant cascades
  • Asset pipeline
  • QA

Data annotation & labeling ops

Labeling programmes that produce usable training data: guidelines, gold sets, inter-annotator agreement, QA sampling and the tooling to run it repeatably.

  • Gold sets
  • QA sampling
  • Tooling

AI-ready data engineering

The unglamorous layer that decides whether AI works: pipelines, modeling, master data and cross-reference resolution so models read data that actually agrees with itself.

  • Pipelines
  • Master data
  • Entity matching

AI governance & audit trails

Making AI output defensible: deterministic validation, approval gates, prompt and version lineage, retention rules, and evidence an auditor can follow end to end.

  • Controls
  • Lineage
  • Approvals

Evaluation & guardrails

Test sets, regression suites and scoring for AI features, plus input/output guardrails — so you can change a model or prompt without gambling on the result.

  • Eval harness
  • Regression
  • Guardrails

Run & optimise

Once it is live: monitoring, drift and quality checks, token and inference cost control, model upgrades, and a support model that keeps the thing dependable.

  • Monitoring
  • Cost control
  • Upgrades

Use cases

Built for teams where a wrong answer has consequences.

Finance, operations and regulated back-office functions — where AI has to be traceable, not just impressive.

Finance & accounting ops

Document-to-ledger automation with validation and controls that stand up in a close cycle and an audit.

Energy & industrial data

Reconciling identifiers and volumes across ERP, production and operations systems so reporting ties out.

Back-office document flow

Contracts, statements, claims and forms turned into structured records without a manual keying team.

Internal knowledge access

One governed assistant over scattered tools and databases, answering with citations and respecting permissions.

Mid-market operations

Fixed-scope AI automation for teams without an internal AI function — audit first, then a contained build.

Creative production at volume

Generated image and video workflows that hold brand consistency across hundreds of variants.

How we work

Audit, pilot, build, run.

Nothing starts with a twelve-month programme. Each stage is scoped so you can stop, and each one produces something you keep.

/ 01 — AUDIT

Find the real candidates

Map the workflow, the data behind it and the control requirements. Output: a costed shortlist and an honest list of what AI should not touch.

/ 02 — PILOT

Prove it on your data

One narrow use case, your documents, measured against a gold set. Output: accuracy numbers and a go/no-go you can defend.

/ 03 — BUILD

Production, with controls

Validation layers, human-in-the-loop queues, write-back, monitoring, deployment scripts and UAT documentation.

/ 04 — RUN

Keep it dependable

Quality and drift monitoring, cost control, model and prompt upgrades, and enhancement work as the process changes.

Why now

The demand is in integration, not demos.

Independent market data on where AI work is actually growing — and why we position at the complex, governed end of it.

+178%
Growth in AI Integration — the fastest-growing coding & web dev category
+50%
Growth in client spend on AI Strategy & Consulting
+45%
Earnings growth for complex, AI-augmented work
−13%
Per-contract decline in commodity generative-AI work

Source: Upwork In-Demand Skills 2026, Future Workforce Index 2026 and Q2 2026 results. Market figures describe the AI services market as a whole — they are not Armour AI X performance metrics.

Start with an audit, not a leap of faith.

Tell us the workflow that is eating your team's week. We will tell you honestly whether AI is the right tool for it — and what it would take.

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