Premal Katigar

Service 01

AI engineering that earns its place in the workflow

Move from an interesting prototype to a dependable system your team can use every day.

What changes

Built for a measurable operating difference.

Grounded answers from private knowledge

Faster review and research cycles

Observable, evaluable AI workflows

Capabilities

A focused technical scope, chosen for the outcome.

01

RAG and knowledge systems

02

LLM application architecture

03

Agentic workflows

04

Evaluation and observability

05

Model and vendor integration

Approach

Evidence before complexity.

  1. 01

    Identify a valuable decision or workflow

  2. 02

    Prototype against representative data

  3. 03

    Measure quality, latency, and cost

  4. 04

    Harden the system for real use

Discuss this service

Bring the problem, not a finished specification.

We can clarify the opportunity and decide whether a focused engagement makes sense.

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