Enterprise AI Implementation

AI implementation services.

InTheCloud helps enterprises move AI initiatives from strategy, pilot and proof of concept into secure production systems — architecture, data and integration, model selection, generative and agentic AI, RAG, security and governance, evaluation, deployment and operations.

We are an AI-first engineering consultancy, and implementation is the whole point. Engagements start with one use case and a measurable number, reach an evaluated working system in 4 to 8 weeks, and land in production within a quarter through your security and governance review — with code, evaluation suites and runbooks owned by your engineers afterwards.

Proof of value: 4 to 8 weeks, fixed scope, small senior team. Ends with a working implementation, evaluation results and a go/no-go recommendation.
Production build: Typically 3 to 6 months per use case, through your security, privacy and model risk review.
Biggest cost driver: Integration surface and data access, not model choice. Clean data and a narrow scope are the cheapest levers you control.
What you keep: Code, prompts, evaluation suites, infrastructure definitions, runbooks and trained engineers. No dependency on us to keep it running.

How we implement enterprise AI

Discovery and framing

We work with your business and engineering leaders to identify AI use cases with clear value, defensible data access and an honest path to production — and reject the ones without them.

Architecture, data and integration

Target architecture, retrieval strategy, integration plan across systems of record, identity and access design, and the cost envelope the system has to run inside.

Build with evaluation attached

A working prototype against your data paired with an evaluation harness, so quality is measurable from day one and model or prompt changes stop being guesswork.

Production engineering

Senior engineers harden the system: APIs, observability, security review, cost and latency controls, and integration with the platforms you already run.

Security, governance and evaluation

PII handling, audit logging, model documentation, release gates and drift monitoring aligned to your AI risk framework and the reviewers who will read it.

Deployment, operations and handover

MLOps, runbooks, on-call expectations, cost attribution and pairing with your engineers so the platform is owned in-house.

Engagement models

AI Proof of Value — 4 to 8 weeks

Fixed scope, one use case, one measurable outcome. Produces a working implementation on real data, evaluation results, a security review and an honest go/no-go recommendation.

Production AI Implementation

One named system taken from validated prototype to production, through integration, security and governance review, with your engineers alongside from the start.

AI Engineering POD

A standing senior pod carrying several AI workstreams at once — platform, retrieval, agents, evaluation — with a shared backlog and a fixed monthly shape.

Enterprise AI Program

Multiple use cases on a shared AI platform: model gateway, retrieval substrate, evaluation tooling, governance and cost controls that product teams build on.

Delivery highlights and engineering guides

What an enterprise agentic AI pilot actually costs

Where the money goes in a first pilot, what drives the range, and why the second one costs materially less.

Read: What an enterprise agentic AI pilot actually costs

Building a production MCP server

Tool design, identity pass-through, idempotent writes and adversarial testing — the integration layer production AI depends on.

Read: Building a production MCP server

An evaluation harness for regulated environments

Golden datasets, rubric grading, CI release gates and drift monitoring — the evidence a risk function asks for before launch.

Read: An evaluation harness for regulated environments

Governed customer data platform

Consolidated fragmented customer data into one governed platform with lineage and access control — the substrate grounded AI reads from.

Read: Governed customer data platform

Bring us the AI initiative you're trying to get into production.

Book a Call with a Builder

What happens next

30-minute builder-led call
No generic sales pitch
Architecture, feasibility and constraints
A recommended next step

Where engagements start: Enterprise AI Proof of Value

A fixed-scope 4–8 week engagement focused on one prioritized use case, producing a working implementation with real enterprise data where appropriate and an evidence-backed decision about production.

— A prioritized use case with a success metric agreed up front
— A target architecture and integration plan for your environment
— A working implementation against your own data
— Model selection and evaluation results, not vendor preference
— A governance and security review with your own reviewers
— A production roadmap and an honest go/no-go recommendation

Discuss an AI Proof of Value

Why do enterprise AI programs stall between pilot and production?

Because the pilot was never scoped to survive review. Permissioned data access, integration with systems of record, audit logging, evaluation a reviewer accepts, cost control at volume and behaviour on failure paths are all discovered after the demo lands.

The fix is structural: pick one workflow with an owner and a number, build the evaluation harness alongside the first version, and involve security and risk in week one rather than month four.

What does the first phase of an AI implementation produce?

A prioritized use case with an agreed success metric, a target architecture, a working implementation against your own data, model evaluation results, a governance and security review, a production roadmap and a go/no-go recommendation.

That includes the recommendation not to proceed. A clean no in week six is a cheaper outcome than a program that runs for a year on optimism.

How do industry and compliance scope change the work?

They change the control surface, not the engineering discipline. Healthcare adds PHI handling and clinical-safety checkpoints; banking adds model risk documentation and SR 11-7 alignment; retail adds peak-traffic and PCI considerations; insurance adds conduct and explainability requirements.

In each case we keep models on retrieval, drafting and triage, put irreversible or regulated actions behind human approval, and make every decision path reproducible from the record.

Frequently asked questions

What do AI implementation services include?

Use-case discovery and framing, data readiness, target architecture, model selection, an evaluated prototype on your own data, integration with existing systems, security and governance review, deployment and operations, then handover to your engineers. The hard part is rarely the model — it is the surrounding software engineering and the review process.

How long does an AI implementation take?

A fixed-scope proof of value typically runs 4 to 8 weeks. Moving from prototype to a production AI system embedded in business operations usually takes 3 to 6 months per use case, depending on data access, compliance scope and integration surface.

Do you work with Anthropic, OpenAI and Google models?

Yes, and we are model-agnostic. We deploy Anthropic Claude, OpenAI GPT, Google Gemini and open-weight models on AWS, Azure and Google Cloud — chosen per workload on latency, cost and evaluation results rather than vendor preference.

Can you ship AI inside regulated environments?

Yes. We operate under SOC 2 Type II and ISO 27001 and routinely ship AI for retail, banking, insurance and healthcare clients, with PII handling, audit logging and governance designed for HIPAA, PCI-DSS and SR 11-7 environments.

What does an AI implementation cost?

A fixed-scope proof of value is priced as a defined first phase; production builds are scoped per use case. Cost is driven by integration surface, data access and compliance scope far more than by the model. Our published cost guide explains how to compare quotes on equal terms.

Do we keep ownership of the system?

Yes. Code, prompts, evaluation suites, infrastructure definitions and documentation are yours, and handover with pairing and runbooks is part of every engagement. Capability transfer is the point, not a lock-in.

Where should an enterprise AI program start?

With one workflow that is high-volume, checkable and owned by someone who wants it — plus a measurable number attached. A portfolio of ten candidate use cases with no owner produces analysis; one owned use case produces a system.