01Agentic AI

Agentic AI & AI Systems

Agents that take real actions in your systems, with the permissions and evals to trust them.

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Works across: Application · Intelligence · Data · Infrastructure

Overview

We build agents and everything they depend on: retrieval over your own data, tool use with scoped permissions, memory, model routing, tuned adapters and private inference. Then we wire it into your stack, trace every step and keep tuning it in production.

FlagshipWant an operator built, hosted and maintained for you? That’s Krews Agent.

What we do

Capabilities

Agents

  • Customer-facing agents
  • Internal operators and copilots
  • Tool use and function calling
  • Multi-step workflows with approvals
  • Voice, image and document input

Knowledge

  • RAG over docs, tickets and databases
  • Hybrid search and reranking
  • SOP and policy ingestion
  • Session and long-term memory
  • Re-indexing when sources change

Models

  • Model selection and routing
  • Open-weight model deployment
  • LoRA and supervised fine-tuning
  • Preference tuning (DPO) where it pays off
  • Evaluation sets and regression tests

Production

  • Private or shared inference
  • GPU serving with vLLM
  • Traces for every agent step
  • Guardrails and human handoff
  • Cost and latency budgets

How we approach it

Principles we hold ourselves to

  1. 01

    Evals before prompts

    Before tuning anything, we write down what a good answer looks like as test cases. The same set gates every release after launch.

  2. 02

    Smallest model that holds up

    Each request goes to the cheapest model that passes the evals. Frontier APIs where they earn it, open-weight models where privacy, cost or control matter more.

  3. 03

    Retrieval is most of the work

    Chunking, metadata, hybrid search, reranking, freshness. Most “the model got it wrong” bugs are retrieval bugs, and we fix them there.

  4. 04

    Actions need permissions

    Tools get scoped credentials, rate limits and audit logs. Anything irreversible can wait for a human.

Signals

You might need this if…

  • Your pilot demos well and falls over on real questions
  • Your data can’t leave your infrastructure
  • Inference costs are growing faster than usage
  • You want an agent that does things, not one that only answers

Works with

  • vLLM
  • Open-weight models
  • Frontier model APIs
  • PostgreSQL + pgvector
  • Redis
  • OpenTelemetry
  • Python
  • TypeScript

Questions

Asked often

Krews Agent is the managed product: we build, host and maintain an operator for your company. An agentic AI engagement is for something custom: an internal system, a feature in your own product, or infrastructure your team will run.

Whichever passes the evaluations at the right cost. Usually a mix: open-weight models on private inference for volume and sensitive data, frontier APIs for hard reasoning.

Yes. LoRA, SFT and preference tuning. We usually check that retrieval and prompting aren’t enough first, because a tuned model is one more thing to maintain.

Related

Usually works alongside

Bring us the hard part.

Tell us what you’re working on. The people who reply are the people who would build it.

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