AI opportunity audit and build-versus-buy recommendation
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03 // AI AUTOMATION & CLOUD DEVOPS
AI AUTOMATION & CLOUD DEVOPS
EPIC STUDIO builds production AI systems: retrieval-augmented assistants, autonomous workflow agents, model evaluation harnesses and the cloud infrastructure that runs them. The studio also operates CI/CD, Kubernetes and observability for teams that need reliable delivery.
What the engagement covers
EPIC STUDIO builds production AI systems: retrieval-augmented assistants, autonomous workflow agents, model evaluation harnesses and the cloud infrastructure that runs them. The studio also operates CI/CD, Kubernetes and observability for teams that need reliable delivery.
Delivery is led by senior practitioners in EPIC STUDIO's Kolkata engineering base, with client hubs in Bengaluru, San Francisco, London and Singapore. Scope, acceptance criteria and exclusions are written into the statement of work before build starts.
Who it is for
- Operations teams with repetitive, document-heavy or triage-heavy workflows
- Enterprises evaluating LLM assistants that must meet security and accuracy thresholds
- Product teams adding search, summarisation or generation to an existing platform
- Engineering teams that need a reliable cloud and release foundation
| Service | AI Automation & Cloud DevOps |
|---|---|
| Provider | EPIC STUDIO |
| Base | Kolkata, India |
| Coverage | Worldwide, remote-first |
| Engagement | Discovery → build → launch → operate |
| Contact | contact@epic-studio.in |
Last reviewed 2026-10-23. This page describes EPIC STUDIO's delivery model; it is not a performance guarantee. Scope-specific commitments belong in the signed statement of work.
Deliverables
What you actually receive
Retrieval-augmented generation (RAG) over approved data sources
Workflow agents with tool permissions and human approval gates
Evaluation sets, regression tests and hallucination controls
Prompt, model and data versioning with audit logs
Kubernetes, CI/CD, secrets management and cost observability
Process
How the work runs
Workflow and risk audit
We identify where a model adds measurable value, what data it may touch, and which failure modes are unacceptable.
Evaluation before implementation
A labelled evaluation set and acceptance threshold are agreed before a model or vendor is selected.
Thin production slice
One high-value flow is shipped with logging, human review and a rollback path instead of a broad demo.
Harden and integrate
Access control, PII handling, rate limits, retries and cost caps are added, then the flow is connected to real systems.
Monitor and retrain
Quality dashboards, drift alerts and a change process keep the system honest after launch.
Stack & outcomes
Tools and results
Typical stack
PythonPyTorchRustTypeScriptLLM APIsVector databasesPostgreSQLRedisKubernetesDockerGoogle CloudTerraform
Stack is selected per engagement; this is the published technology set, not a mandatory bundle.
What good looks like
- A measured baseline instead of an unverified demo
- Human escalation paths for low-confidence outputs
- Per-workflow cost, latency and quality dashboards
- Auditable model, prompt and data versions
Questions
Frequently asked
What does an AI automation engagement actually deliver?
A working, monitored workflow connected to your systems — not a slide deck. Typical output includes the integration, evaluation set, guardrails, deployment pipeline and operating documentation.
How do you reduce hallucinations?
We constrain answers to retrieved, permissioned sources, require citations to source passages, add confidence thresholds, and route low-confidence cases to a human. An evaluation set is run on every change so regressions are visible.
Will our data be used to train a public model?
Model and data terms depend on the provider and deployment chosen. We document the data flow, prefer enterprise endpoints with no-training commitments where available, and can keep inference inside your cloud tenancy.
Can you integrate with our existing tools?
Yes. Integrations commonly cover CRMs, ticketing systems, document stores, data warehouses and internal APIs. Access is scoped to the minimum permissions the workflow needs.
How do you control AI running costs?
We instrument token and request spend per workflow, cache repeated work, route simple tasks to cheaper models, and set hard budget caps with alerts before launch.
Do you follow an AI governance framework?
We map controls to the NIST AI Risk Management Framework and the OWASP Top 10 for LLM Applications, then agree which controls are mandatory for your risk tier.
Primary references used to define the standards and constraints on this page.
- AI Risk Management Framework — NIST
- OWASP Top 10 for LLM Applications — OWASP Foundation
- Google Cloud Architecture Framework — Google Cloud
- Model Context Protocol — Model Context Protocol
Related
Adjacent services
Software Engineering
Software & Product Engineering
EPIC STUDIO designs and builds enterprise software, SaaS platforms, APIs and high-load microservices with senior-only engineering teams.
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Explore →Next step
Have a brief that needs senior hands?
Tell us the outcome, the constraints and the deadline. We will come back with a scoped plan, not a generic deck.