Generative AI Services for the Enterprise
Most enterprise generative AI never leaves the pilot. CreateOS takes your GenAI use case to governed production on the unified AI execution layer, with forward-deployed engineers who embed with your team. Every request routed, policy-checked, validated, and audited.
- ISO 27001 and SOC 2 Type II certified
- 100+ models, model-agnostic
- Cloud, VPC, or on-prem
- Governed and auditable by default
The Gap is Production, Not the Model
Generative AI demos well and stalls at the security review. Closing that gap is the job.
of enterprise AI pilots never reach production.
MIT NANDA, 2025
average breach cost in financial services, the exposure ungoverned GenAI adds.
IBM, 2025
of new cyber-insurance now excludes ungoverned AI risk.
Munich Re, 2026
What We Deliver
Generative AI built for production from the first call, not a demo that stalls at security review.
GenAI strategy and use-case discovery
We pick the highest-impact use case, make the business case, and produce a build spec and production roadmap before anything is built.
LLM integration, routing and evaluation
Connect to any of 100+ models, routed by task and benchmarked against your data, not generic leaderboards. Swap providers without a rebuild.
RAG and enterprise knowledge
Retrieval that grounds every answer in your own documents and systems of record, with citations, freshness, and access controls.
AI agents and multi-agent systems
Agents that plan and execute multi-step work, single or coordinated teams, each scoped to an autonomy level and checked against policy.
Governance, safety and output validation
Prompt-injection checks, policy enforcement, PII masking, and hallucination validation on every call. Responsible GenAI by default.
LLMOps: deployment, evaluation and observability
Decision lineage, evaluation harnesses, cost telemetry, and a full audit trail, so a GenAI system stays reliable after it ships.
Generative AI Agents We Put into Production
Common generative AI agents and workloads we take live on the execution layer, each governed, cited, and auditable.
Customer service agents
Resolve the full ticket lifecycle with governed responses, PII masked, and a clean handoff to a person when an agent should not answer.
Marketing and content agents
A governed sub-agent system across content, planning, channel adaptation, and publishing. Every outbound artifact reads one brand-voice file and clears a human gate.
SEO agents
Technical SEO audits, keyword and search-gap analysis, and content optimization, run continuously and grounded in your own analytics and search data.
Personalization agents
Tailor recommendations and experiences from your own data, grounded and explainable, scoped to what each user is allowed to see.
Productivity and insights copilots
Board packs, forecasts, and summaries pulled from systems of record, every figure traced to source.
Knowledge assistants
Answers grounded in approved internal sources, scoped per role, with no cross-boundary leakage.
Document and contract intelligence
Read mixed files in full, extract clauses and obligations, and surface what bears on the decision, every fact cited.
Data extraction and processing
Turn unstructured documents, images, and streams into structured, governed output at scale.
The Marketing OS we run, on CreateOS
Our own marketing runs as a governed sub-agent system on CreateOS: many agents behaving like one team, behind human gates. The same structure we build for customers. 21 workflows across 6 archetypes, every outbound artifact traced to its workflow and approver.
Content
Release notes, product updates, blog drafts, and brand video, drafted from your own sources.
Knowledge
Brand-voice extraction and enforcement, repetition checks, and competitor and customer signal synthesis.
Localization
One source adapted to every channel, persona, and funnel stage in its native shape.
Analyzer
Weekly performance digests, funnel attribution, and search-gap analysis that read your real data.
Planner
Content calendars, launch sequences, and quarterly themes.
Operator
Scheduled multi-channel publishing, launch-day orchestration, and reply routing, each behind an approval gate.
Human gates on everything outbound, a brand-voice file as a hard dependency, sandboxed research, and every artifact traced to its workflow and approver.
How an Engagement Works
A staged path from concept to governed production. Value lands early and governance holds at every step.
- 01
Discover
We pick the highest-impact GenAI use case, scope it, and produce a build spec and production roadmap. Fixed pricing agreed in writing.
- 02
Prove
We stand up a scoped pilot on the execution layer, governed from the first call, to prove value against your own data.
- 03
Productionize
Forward-deployed engineers harden it: routing, policy enforcement, output validation, evaluation, and a full audit trail.
- 04
Scale and support
It goes live, then spreads. Model lifecycle management, monitoring, and improvement on the layer you keep.
Why CreateOS for Generative AI
Governed from day one
Policy enforcement, output validation, and a full audit trail are on from the first call, not bolted on before the security review.
The platform stays after we leave
Your GenAI runs on the unified AI execution layer we build and operate. The engagement ends; the governed layer stays.
Model-agnostic
Not locked to any provider. We benchmark models against your data and swap them without rebuilding your product.
Embedded, not outsourced
Forward-deployed engineers work inside your team and environment, not from behind a ticket queue. You own all the IP.
Common Questions
What are generative AI services at CreateOS?
An engagement that takes a generative AI use case from concept to governed production. Forward-deployed engineers handle strategy, LLM integration and routing, RAG, agents, governance, and LLMOps, and ship on the CreateOS unified AI execution layer so the result is routed, policy-checked, validated, and audited on every call.
How is this different from a generic GenAI consultancy?
A consultancy hands you a build on tools it does not control and leaves. CreateOS delivers on the execution layer we operate ourselves, so routing, governance, output validation, and audit stay enforced after the engineers roll off. You also own all code, models, and IP outright.
We already started a GenAI pilot. Can you take it to production?
Yes, that is the most common engagement. Bring work from any framework or builder. We wire it into the governed path, add the policy enforcement and audit trail your security team requires, and stand it up in your environment.
How do you keep generative AI from hallucinating or leaking data?
Output validation, hallucination checks, and PII masking run on every response before it reaches a user, and answers are grounded in your sources with citations. Access is scoped to least privilege and every call is logged, with zero data retention by default.
Which models do you use?
Any of them. Through the CreateOS router we route across 100+ models including GPT, Claude, Mistral, LLaMA, Deepseek, and Gemini, selected by task and benchmarked against your data, with automatic failover and no lock-in.
How fast can a generative AI use case reach production?
The standard rollout is 12 weeks across gated phases of escalating autonomy. The fastest comparable deployment went from pilot to full production in 75 days. Simpler use cases can go live in under two weeks.
Where does it run?
CreateOS cloud, your VPC, or fully on-prem, with region-aware compute and zero data retention, so regulated data never crosses a border you did not approve.
Do you run generative AI agents yourselves?
Yes. Our own marketing runs as a governed sub-agent system on CreateOS: 21 workflows across six archetypes (content, knowledge, localization, analyzer, planner, operator), with human gates on everything outbound and a shared brand-voice file. We build for customers what we run ourselves.
Where do you want to start?
Bring one stuck generative AI pilot. We will take it to governed production on the execution layer.