Generative AI, agents and LLMs at scale
Enterprise AI services — take generative AI, AI agents and LLMs from isolated pilots to production across the organisation, with the governance, platform, integration and operating model to keep enterprise AI reliable, compliant and cost-controlled.
Quick answerEnterprise AI services take AI from isolated pilots to production at scale across the organisation — with a defined AI operating model and governance, a shared platform and reference architecture, secure integration into enterprise systems, LLMOps and cost controls, and a delivery model that reuses components across use cases rather than rebuilding each time. It is the cross-cutting layer that lets every AI service run together.
What an enterprise AI platform provides
So each new use case plugs in, instead of re-integrating and re-governing from scratch.
Governed LLM gateway
One endpoint for every app — routing and fallback, central prompt and policy control, per-team cost tracking and quotas, caching and logging.
Shared retrieval / RAG service
A single grounding layer any use case can call, with permission-aware retrieval and citations.
Prompt & evaluation registry
Versioned prompts and evaluation sets, so quality is measured and regressions are caught.
Secure connectors
A governed connector layer to enterprise systems, with identity and policy enforcement.
Observability & FinOps for AI
Quality, latency and spend per use case, with GPU right-sizing for any self-hosted models.
Reusable UX components
Assistant, search and copilot patterns teams assemble rather than rebuild.
Tiered governance that enables delivery
| Use case risk | Path | Controls |
|---|---|---|
| Low — internal, non-sensitive | Lightweight self-service | Acceptable-use policy, logging, cost guardrails |
| Medium — internal, sensitive data | Standard review | Data classification, evaluation standards, human oversight |
| High — customer-facing or high-impact | Full review gate | Red-teaming, model risk management, audit, sign-off |
The AI services that run on it
Custom GenAI and LLM solutions delivered on shared platform components.
Agents and agentic workflows with central guardrails and observability.
Chatbots and knowledge assistants on the shared retrieval service.
The governed connector and gateway layer itself.
The operating model, roadmap and readiness work that stands it up.
Dedicated engineers to run the platform and deliver use cases.
From pilots to a scaled AI capability
Assess
AI readiness, the use case pipeline, and the target operating model.
Foundation
Stand up the gateway, retrieval service, governance and observability.
Migrate
Bring existing pilots onto the platform; retire bespoke plumbing.
Scale
Deliver new use cases on shared components, with tiered review.
Run
Operate as a managed service, or hand off a self-sufficient model.
An enterprise AI partner with platforms and operations
RLCatalyst & Spectra
AI operations and data analytics platforms behind the delivery team.
GenAI framework for AIOps & SRE
Field-tested patterns for running AI reliably at scale.
AWS-native
Bedrock, SageMaker and GPU infrastructure within your account and controls.
FinOps for AI
Model routing, caching, budgets and cost-per-use reporting so spend is owned.
Strategy through to run
One partner from operating model design to managed operations.
Enterprise scale
350+ Data & AI specialists and 150+ delivered projects across regulated industries.
Explore any capability in depth
Generative AI Services
GenAI consulting, development and implementation.
ExploreAI Development Services
Custom AI and machine learning engineering.
ExploreAI Consulting Services
Strategy, roadmap, readiness and ROI.
ExploreAI Agent Development Services
Autonomous and agentic AI.
ExploreLLM Development Services
Custom, fine-tuned and private LLMs.
ExploreAI Integration Services
Connect AI into your enterprise stack.
ExploreRAG Development Services
Retrieval-augmented generation solutions.
ExploreHire AI Developers
Dedicated AI, LLM, agent and RAG engineers.
ExploreEnterprise AI, tuned by industry
Governance, data-sensitivity and audit requirements shape how AI scales in each sector.
Financial services
Document AI, risk and fraud models, and governed copilots that meet audit and compliance needs.
Healthcare & life sciences
Clinical and research assistants, knowledge retrieval and automation with data governance built in.
Retail & consumer
Personalization, demand forecasting, and customer and associate assistants across channels.
Manufacturing & supply chain
Predictive maintenance, quality vision, and agentic workflows for planning and operations.
Public sector
Secure, private-LLM deployments for citizen services, case work and knowledge access.
Technology & ISVs
Embed generative AI features, agents and RAG into your product with a partner who ships.
- AWS
- DataStax
- Salesforce
- Snowflake
Scaling AI in the enterprise
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Read MoreEnterprise AI Services: frequently asked questions
Content last reviewed: September 2026
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