ZealVector designs and builds practical AI for products in production
— model and API integration, agent workflows, retrieval and knowledge systems,
and the evaluation to trust them.
The weekly digest is our research arm. Every week
we break down what actually shipped in AI — strategy, impact on humans, what to watch
for. The same discipline decides what goes into your product: pieces proven in the field,
not press releases.
What we build
Four kinds of work. Every card flips — the front is the
promise, the back is what ships.
Integration
Model & API integration
Language and vision models wired into your product —
the provider handled, the context designed, the responses streamed.
Click for what ships →Middle: flip · sides: prev / next
Model & API integration
What ships
Provider integration and key management
Prompt and context design for your domain
Streaming responses with graceful fallbacks
Cost and latency budgets, enforced in code
← Click to flip backMiddle: flip back · sides: prev / next
Agents
Agent workflows
Multi-step automation that uses your tools and data —
and asks permission before it does anything that matters.
Click for what ships →Middle: flip · sides: prev / next
Agent workflows
What ships
Tool and function design around your systems
Orchestration logic for multi-step tasks
Human-in-the-loop approval gates
Guardrails and explicit failure paths
← Click to flip backMiddle: flip back · sides: prev / next
Knowledge
Retrieval & knowledge systems
Your documents and data answering questions with sources
— not confident guesses.
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Retrieval & knowledge systems
What ships
Ingestion and chunking pipeline for your content
Embeddings with hybrid search
Grounded answers with citations
Freshness and access controls
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Evaluation
Evaluation & reliability
Proof the system works before your users find out it
doesn’t.
Click for what ships →Middle: flip · sides: prev / next
Evaluation & reliability
What ships
Eval suites built on your real cases
Regression gates in your CI
Tracing and monitoring in production
A playbook for model upgrades
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How an engagement runs
Small steps, each one useful on its own.
01
Scope
A working session on your product and data. You leave with a written plan you can
act on — with or without us.
02
Prototype
A thin slice built on your real data. It answers the only question that matters:
does this work here?
03
Integrate
The slice becomes a feature — wired into your stack, your auth, your UI.
Production paths, not demo paths.
04
Operate
Evals, monitoring, and a clean handover. Your team runs it; we stay reachable.
Start with one email.
Tell us the product, the problem, and where AI is supposed to
help. We reply with questions, not a pitch deck.
