Thread 02 · Haldi · turmeric
AI & Automation
Practical AI engineering: LLM-powered features, retrieval (RAG), agents and automation of document and operational workflows: measured, guarded and cost-controlled.
Swatch · ai-automationSatin 5
Who it’s for
Products adding AI features
You want assistants, search or generation inside your product, and you need them accurate, fast and affordable at scale.
Operations drowning in documents
Invoices, contracts, forms and emails are processed by hand. You want them read, checked and routed automatically.
Teams past the demo stage
The prototype impressed everyone. Now it needs evaluations, guardrails, monitoring and a budget before it meets customers.
What you get
- 01AI features chosen for measurable value, not novelty
- 02Answers grounded in your own data, with sources your users can check
- 03Evaluation suites that catch regressions before a model change ships
- 04Predictable costs per task, with fallbacks when a provider is slow or down
Deliverables
- AI opportunity discovery
- Retrieval (RAG) over your data
- Agents & tool use
- Evaluations & guardrails
- Document & workflow automation
- Cost & latency monitoring
Materials
Tools we reach for first, chosen per project and never by habit.
- Claude
- OpenAI
- pgvector
- LangGraph
- Python
- TypeScript
How we weave it
- Pass 01
Find the real job
We identify the tasks where AI saves hours or unlocks revenue, and the ones where plain software is the better tool.
- Pass 02
Build the evaluation first
Before prompts, we collect real examples and define what a good answer looks like. Every change is measured against them.
- Pass 03
Ground and guard
Retrieval over your data, structured outputs, permission checks and human review where the stakes demand it.
- Pass 04
Run it in the open
Tracing, cost dashboards and feedback loops in production, so quality and spend stay visible after launch.
Questions, answered
Which AI models do you work with?
We are model-agnostic and pick per task: typically Anthropic's Claude and OpenAI models, plus open-weight models where data residency or cost requires it. Our architecture keeps you free to switch providers.
Is our data used to train AI models?
No. We use provider APIs and settings that exclude your data from training, keep data in the regions you require, and design retrieval so users only ever see what they are permitted to see.
How do you stop AI from making things up?
By grounding answers in your documents with citations, constraining outputs to structured formats, running automated evaluations on every change, and routing low-confidence cases to a person.
What does AI cost to run?
It depends on volume and model choice. We estimate cost per task during discovery, then use caching, smaller models for simple steps and batching to keep it predictable, and we monitor it after launch.