AI Engineering · GenAI Delivery Factory Built to ship. Governed to stay.
Most GenAI work stalls between proof of concept (PoC) and production. Our GenAI Delivery Factory is the bridge: we design, build, evaluate and ship, with every step governed from blueprint to production.
The factory model
Months of building, compressed into weeks of agentic deployment.
We use agent skills, mature frameworks and accelerators to deliver fast.
- 01Our Accelerated Delivery Frameworks and AI Eval Factory embed evaluations, governance and quality gates at design time — not after the fact.
- 02Engineers with a deep understanding of the AI and GenAI software development lifecycle (SDLC) work embedded in your teams to build and deliver agentic workloads.
- 03Our engineering services span the AI SDLC — from consulting to QA of GenAI applications, RAG and agentic workflows.
- 04We don’t deliver IT projects. We deliver agent skills — built, evaluated, governed and deployed through close collaboration between people and AI.
What the factory delivers
Four service lines, one governed pipeline.
AI co-workers for human productivity
AI co-workers that improve efficiency, decision-making and customer experience — validated in a PoC phase, then scaled to complex use cases with enterprise integration.
Deployment and operations (LLMOps)
LLMOps frameworks for scalable, reliable operations — infrastructure setup, model lifecycle management, performance monitoring and scaling.
Observability, evaluation and performance
Agents operate with a preconfigured level of autonomy and can behave non-deterministically. Continuous monitoring tracks their actions and their consistency with organisational policy and responsible AI standards.
AI security and governance
Defences against evolving AI threat vectors — prompt injection, jailbreak attempts and data exfiltration — with GDPR and DPDP Act compliance built in.
From PoC to production
Validate first. Then scale.
/ 01
PoC phase
Validate concepts through rapid prototyping, and confirm they align with business objectives, deliver measurable business value and remain feasible long term.
/ 02
Production phase
Scale the solution to complex use cases, with improved accuracy and close integration with enterprise systems for real-world adoption.
/ 03
Operate and observe
LLMOps with continuous monitoring, evaluation and auditing keeps shipped AI compliant as it scales.
Security and governance controls
Governance built in, not bolted on.
Automated PII detection and redaction
Integration with enterprise SIEM and SOC systems
Compliance with GDPR and India’s DPDP Act
Human-in-the-loop (HITL) controls to mitigate critical risks
Transparency and explainability for high-risk applications
See how and why a model generates a particular output.
Where it fits
The SDLC every AI system we build ships through.
The Delivery Factory is where Future of Work agents, RAG systems and customised models reach production — scored by Agent Evaluations, connected through custom MCP, and accelerated by our AI-orchestrated delivery workflow.
FAQ
Frequently asked.
01What is the GenAI Delivery Factory?
Our GenAI delivery model, from blueprint to production: design, build, evaluate, and ship enterprise-grade AI from blueprint to production. Most GenAI work stalls between proof of concept and production; the factory is the bridge.
02How is humaineeti different from a typical IT services firm?
We don’t deliver IT projects. We deliver agent skills — built, evaluated, governed, and deployed through close collaboration between people and AI. Production-grade from day one.
03Does humaineeti’s GenAI work comply with GDPR and the DPDP Act?
The delivery factory builds in AI security and governance from design time, with controls designed to support GDPR (EU) and DPDP Act 2023 (India) obligations.
04What does “governed to stay” mean?
Every step of the factory — design, build, evaluate, ship — is governed. Once shipped, the AI continues to be monitored, evaluated, and audited so it stays compliant as it scales.
Related guides
Keep reading.
GenAI Operations · 7 min
LLMOps in Production
Monitoring, evaluation, guardrails, and governance for enterprise generative AI at scale.
Read the guide
AI Engineering · 6 min
RAG vs Fine-Tuning
When to use retrieval augmented generation versus fine-tuning for enterprise LLM applications.
Read the guide
AI Engineering · 8 min
What is Agentic AI?
How autonomous AI agents differ from traditional automation — and why enterprises need them.
Read the guideNext step
Bridge the gap to production.
Bring the proof of concept that stalled, or the use case you haven’t started. We’ll show you how the factory designs, builds, evaluates and ships it, with every step governed.