AI Engineering Services

Enterprise AI engineering services.

InTheCloud provides AI engineering services for enterprises — AI application, generative and agentic engineering, RAG and enterprise search, MCP integrations, AI APIs and data pipelines, evaluation, cloud architecture and production reliability. We build the systems around the model.

We are a senior AI engineering practice, not a strategy firm. Engagements are staffed with engineers who have shipped enterprise platforms, run as an integrated pod alongside your team, and end with an observable, supportable system in production — plus the evaluation suites and runbooks your engineers own afterwards.

Positioning: We build the systems around the model — retrieval, integrations, orchestration, evaluation, observability and the platform underneath.
Clouds: AWS, Azure and Google Cloud, with models deployed through Bedrock, Azure OpenAI or Vertex inside your own network boundary.
Engagement models: AI proof of value, production implementation, AI engineering pod, or an embedded team inside your platform group.
Typical timeline: 4 to 8 weeks to an evaluated working system; production within a quarter for a single well-scoped workload.

How we engineer AI systems

Architecture before prompts

We design the data flow, retrieval strategy, tool surface, failure behaviour and cost envelope first — the decisions that determine whether the system survives contact with real traffic.

Build with evaluation attached

Every workload ships with golden datasets and graders in CI, so quality is a measured number and prompt or model changes stop being a leap of faith.

Harden for production

Observability, cost and latency budgets, security review, IAM and key management, graceful degradation, and integration with the platforms you already run.

Hand over the capability

Runbooks, eval suites, architecture decision records and pairing, so your engineers own the system rather than renting it from us.

What our AI engineering teams build

AI application engineering

Product surfaces built properly: streaming interfaces, structured outputs, retries and timeouts, permissioned data access, and a non-AI fallback path on every user-facing feature.

Generative AI engineering

Drafting, extraction, classification and summarization services with typed schemas, validation on every model response, and small-model routing where accuracy holds.

Agentic AI engineering

Plan-act-observe loops with step budgets, typed tool surfaces, idempotent writes and approval gates — inspectable rather than emergent behaviour.

RAG and enterprise search

Hybrid keyword and embedding retrieval, chunking tuned to the corpus, access control applied at query time, citations on every answer, and relevance measured on your own queries.

MCP and enterprise integrations

MCP servers and service APIs over existing systems, with identity pass-through, least-privilege scopes, schema validation and full tool-call logging.

AI APIs, data pipelines and platform

Ingestion and embedding pipelines, model gateways, prompt and tool registries, vector stores and shared cost controls so multiple product teams can build on one AI platform.

Evaluation and testing

Golden datasets, rubric graders calibrated against human reviewers, adversarial cases, CI release gates and drift monitoring on live traffic.

Cloud AI architecture and security

VPC deployment, KMS-managed keys, IAM scoping, no egress to public model endpoints where policy forbids it, and audit logging designed for the reviewers who will read it.

Engineering guides from production work

Building a production MCP server

Tool design, identity pass-through, idempotent writes and adversarial testing — the integration engineering that decides whether AI can act on systems of record.

Read: Building a production MCP server

An evaluation harness for regulated environments

Golden datasets, rubric grading, CI release gates and drift monitoring — the suite that lets a team change prompts weekly without a committee.

Read: An evaluation harness for regulated environments

Hardening a GraphQL storefront

Hardened the API and data layer AI features depend on — the reliability work that decides whether an assistant holds up at peak.

Read: Hardening a GraphQL storefront

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 AI prototypes fail to reach production?

Because the prototype solved the easy half. The hard half is permissioned data access, integration with systems of record, evaluation that a reviewer accepts, observability, cost control at volume, and behaviour on the failure paths nobody demoed.

That work is ordinary senior software engineering applied to a new component. It is also why a team that only writes prompts stalls at the security review, and why we staff engagements with engineers who have shipped platforms before.

What does 'we build the systems around the model' mean in practice?

It means the model is one dependency among many. A working AI feature needs retrieval with access control, schema-validated outputs, idempotent writes, tracing, budgets, fallbacks and a release process — and each of those is code we write and hand over.

It also means we will tell you when a smaller model, a classifier or a plain database query is the right answer. The cheapest AI engineering is the work you do not have to run.

How do you control AI cost at scale?

By treating tokens as infrastructure spend. Classification and extraction go to small models, the main workload to a mid-tier model, and only genuine hard cases escalate — with the escalation rule set by the eval suite rather than by instinct.

Stable context is cached, budgets for cost and latency are enforced per request at the gateway, and spend is attributed per feature so a regression shows up on a dashboard rather than on an invoice.

Frequently asked questions

What are AI engineering services?

AI engineering services cover the software around the model: data pipelines, retrieval, model integration, agent orchestration, evaluation, observability, security and the platform engineering that lets AI run reliably in a real enterprise environment. The model is a component; the system is the deliverable.

How is AI engineering different from AI strategy consulting?

Strategy consulting produces recommendations. AI engineering ships software. Our engagements end with a working, observable and supportable system in production, plus the runbooks and evaluation suites your team needs to keep changing it.

Which AI models and clouds do you build on?

We are model- and cloud-agnostic. We deploy Anthropic Claude, OpenAI GPT, Google Gemini and open-weight models on AWS, Azure and Google Cloud. Choices are made per workload — latency, cost, evaluation results, data residency — not by vendor preference.

Do you embed with our existing engineering team?

Yes. Most engagements run as an integrated pod alongside your engineers, transferring knowledge as we build and leaving behind documentation, runbooks and trained in-house teams. Capability transfer is part of every project.

What engagement models do you offer?

An AI proof of value (4 to 8 weeks, fixed scope), a production implementation for a named system, an AI engineering pod that carries several workstreams, or an embedded engineering team inside an existing platform group.

How do you make AI systems reliable in production?

Evaluation suites gating releases, tracing on prompts and tool calls, per-request cost and latency budgets, cheap-first model routing, caching on stable context, graceful degradation to a non-AI path, and alerting on drift rather than only on errors.