How we work: forward-deployed engineering for enterprise AI
Building agents is solved. Running them governed, at scale, in production is not. Our engineers embed with your team and take stuck proofs of concept to governed production on the CreateOS platform, in weeks.
- ISO 27001 certified, SOC 2 Type II attested
- 3 years in production AI
- Every major model provider
- Model-agnostic by default
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Agent
Support Triage
Duration
3.4s
Steps
5
Tokens
4,912
Status
Succeeded
- Support Triage3.4s
- Guardrail · input120ms
- Search · Support Docs410ms
- Gmail · read thread480ms
- openai/gpt-4o2.1s
- Guardrail · output260ms
A platform you run, and engineers who get it to production.
Building agents is solved. Running them, governed and at scale, is the unsolved 90%. We walk the last mile with you.
- Platform plus forward-deployed engineeringOur engineers embed, re-architect the process for agents, and do the context engineering that makes them reliable.
- Start with one stuck agentPoint us at a stuck, high-stakes agent. We get it to governed production in weeks, then it spreads.
- Delivered at scaleImplementation partners deliver with us, so onboarding speed does not cap out at how fast we can hire.
Time to first production agent, measured in weeks.
The platform under the engagement
Every engagement delivers on the same stack: data and integrations at the base, agent deployment at the top. The engineers roll off; the governed layer stays.
- 01Agent DeploymentRAG, workflow, and advanced orchestration for multi-agent teams.
- 02Application ExperienceLow-code UI generation and adaptive frontend delivery.
- 03Workflow AutomationVisual builders, parallel branching, and inline code execution.
- 04Context and KnowledgeSemantic mapping and enterprise knowledge graphs.
- 05Data and OntologyDistributed stream processing into a golden system of record.
- 06Embedded IntegrationsGoverned connectors to enterprise systems like SAP, Salesforce, and Workday.
What an engagement builds
Six capability areas, one accountable team. Scoped to the workflow, delivered to production.
- Legal · Financial Services · OperationsAI Agents & Multi-Agent SystemsAgents that reason, plan, and execute multi-step work, governed and auditable at every step. Single agents or coordinated teams.
- Legal · Healthcare · Financial ServicesRAG & Enterprise KnowledgeRetrieval-Augmented Generation that grounds every answer in your own documents and data, with citations and access controls.
- Technology · EnterpriseLLM Integration, Routing & EvaluationConnect your product or internal tools to models from every major provider, routed by task and benchmarked against your actual use case before you commit.
- Manufacturing · Healthcare · LogisticsComputer Vision & Industrial AITurn video and images into governed operational intelligence, on-premise or at the edge.
- Financial Services · Enterprise · ManufacturingData Engineering, ML & AI PipelinesThe foundation that makes AI reliable in production: clean data, fast retrieval, custom models, automated model ops.
- All industriesAI Strategy & RoadmapFor teams that know AI matters but need the proof of concept picked, the case made, and the rollout sequenced before anything is built.
Generative AI services
A focused engagement for enterprise generative AI: from a stuck project to governed production on the unified AI execution layer. Strategy, RAG, agents, model routing, and LLMOps, delivered by forward-deployed engineers.
Discover, prove, productionize
From your first agent to production in 12 weeks. Three phases, each ending at a gate your security and finance teams sign off, so value lands early and governance holds at every step.
- 01Weeks 1-2 · ScopingDiscoverOne workflow chosen, its blocking gate named, and the plan agreed.
Gate: A written scope: deliverables, success criteria and cost.
- 02Weeks 3-6 · SupervisedProveThe proof of concept runs on your systems, against the agreed criteria.
Gate: Success criteria met; policies enforced on every agent call.
- 03Weeks 7-12 · GovernedProductionizeIn governed production, with autonomy widened only as the controls hold.
Gate: Production sign-off from security and finance.
In 12 weeks: one workflow in governed production, signed off at every gate.
An accountable partner, not a tool vendor
- 01The platform under the engagementYour agents run on CreateOS, the unified AI execution layer we build and operate ourselves. The engagement ends; the governed layer stays.
- 02Embedded, not outsourcedForward-deployed engineers work inside your team and your environment, not from behind a ticket queue.
- 03Governed from day onePolicy enforcement, output validation, and a full audit trail are on from the first call, not bolted on before the audit.
- 04Model-agnostic by defaultNot locked to any provider. We benchmark models against your actual data and swap them without rebuilding your product.
- 05Fixed pricing, no surprisesDeliverables, timeline, and cost agreed in writing before we start. No retainer ambiguity, no hourly overruns.
- 06You own everythingAll IP, code, and models are yours outright. We document everything and train your team so you are never dependent on us.
The engagement, from proof of concept to scale
- 01Discover, weeks 1 to 2Point us at one stuck, high-stakes workflow. Free discovery call, no commitment. Deliverables, timeline, and fixed pricing in writing before we start.
- 02Prove, weeks 3 to 6Our engineers embed with your team, re-architect the process for agents, and do the context engineering that makes them reliable. Progress visible every week.
- 03Productionize, weeks 7 to 12The workflow goes live on the execution layer, policy enforced and fully logged. Then it spreads to the next one.
Tech we work with
- OpenAI GPT
- Anthropic Claude
- Mistral
- LLaMA
- Deepseek
- Google Gemini
- Cohere
- LangChain
- LlamaIndex
- PyTorch
- TensorFlow
- Pinecone
- Weaviate
- AWS Bedrock
- Azure AI
- Google Cloud AI
- Next.js
- Node.js
- Python
- Docker
- Kubernetes
- MLflow
Selected outcomes
- 3x
Faster booking resolution vs. manual process
WhatsApp AI assistant and booking engine for a client services company
Built a three-layer agent system: a booking engine, WhatsApp AI assistant via Meta Cloud API, and a real-time ops dashboard, all routing through a unified LLM layer.
- 60%
Reduction in model-related operational overhead
Unified LLM API layer with RAG and cost tracking for an AI SaaS platform
Integrated 100+ LLM models into a single routing layer with RAG knowledge retrieval, automatic fallback handling, and per-model cost tracking.
- 3 years
Running in production for an institutional client
Full-stack AI deployment platform for an institutional infrastructure client
Designed and deployed a cloud infrastructure and scaling platform requiring enterprise-grade reliability and consistent uptime across all production environments.
What customers say
“Nothing should come between a doctor and patient care, especially when timely medical attention matters. At Davalogy, every minute counts. CreateOS has enabled us with a secure, scalable digital infrastructure that brings together patient, doctor and staff portals, unified electronic medical records, real-time vitals, consultation history and automated WhatsApp notifications. With role-based access and privacy-focused data processing, we can access critical patient information without depending on fragmented records or manual coordination. Technology should give doctors more time to care, not more systems to manage and that is exactly what we are building with CreateOS.”
Dr. Gaurav Kohli, Founder · Davalogy “Agent Studio was built to shrink the distance between an idea and a live agent. Deployment was the last step where developers still had to stop and deal with infrastructure. NodeOps closes that gap. Select it and your agent is live.”
Yang, Solution Architect, AI & Privacy · BNB Chain “The team understood our infrastructure requirements from day one. What would have taken us months internally, they shipped in weeks and it held up in production.”
Head of Infrastructure · Maven11 “We evaluated three AI vendors. CreateOS was the only one that came back with a proposal grounded in our actual data environment, not a generic pitch deck.”
Product Lead · Kodeus “Building a product in just four to five hours is not a joke. The speed and the quality were genuinely impressive. Everything was smooth, clear and well executed.”
Kalash Vasaniya · Justref “This entire thing would have taken me a week very easily. To finalise everything, to find people who can build an app for me, to curate everything in one place. So it's really great.”
Gunjan · Riyasat “I thought it would be much more difficult. Even a person like me, who does not understand the creation or programming of sites, is very facilitated by this.”
Stefania · TACBoard “I love the fact that it has both the mobile UI and the laptop UI at the same place.”
Ankit · AnyWhereGYM
Common questions
What is forward-deployed engineering?
Forward-deployed engineering means our engineers embed with your team to take a specific workflow from a stuck project to governed production. They re-architect the process for agents, do the context engineering that makes them reliable, and ship on the CreateOS execution layer. You get platform plus people, not a license and a wish of good luck.
How is this different from hiring an AI agency?
An agency hands you a build on tools it doesn't control and leaves. A forward-deployed engagement delivers on the execution layer we operate ourselves: routing, governance, output validation, and audit stay enforced after the engineers roll off. The engagement ends; the governed platform stays.
We already built agents. Can you take them to production?
Yes, that is the most common engagement. Bring agents from any framework or builder. We wire them into the governed path, add the policy enforcement and audit trail your security team requires, and stand them up in your environment.
How long until the first agent is in production?
The standard rollout is 12 weeks in three gated phases: Discover (weeks 1 to 2), Prove (weeks 3 to 6) and Productionize (weeks 7 to 12). An early manufacturing proof of concept ran for 75 days across four states. Simpler integrations can go live in under two weeks.
Do you work with companies new to AI?
Yes. Those engagements start with AI Strategy & Roadmap: we assess your current setup, pick the highest-impact proof of concept, and produce a concrete build spec before any development begins. You do not need to know what to build. That is our job.
Which LLMs do you work with?
All of them. Through the CreateOS router we have access to models from every major provider, including GPT, Claude, Mistral, LLaMA, Deepseek, and Gemini. We evaluate and recommend based on your requirements, not partnerships.
Do we own everything that gets built?
Yes. All code, models, data pipelines, and IP are yours outright. We document everything and can train your internal team to manage what we have built.
How does pricing work?
Fixed-scope engagement pricing, not hourly retainers. Every engagement starts with a discovery call and a proposal specifying deliverables, timeline, and a fixed cost. Larger ongoing builds run on milestone-based contracts.
Bring us one stuck workflow.
We take it to governed production with you, on your systems, in weeks, not quarters.