Studio open for new work

We build systemsthat work.

Krews is a digital systems and AI engineering studio. We build agentic AI, web products and the infrastructure underneath. Then we wire it into your stack and keep it working.

01What Krews does

A small studio of engineers and designers working across the whole stack. We plan it, build it, ship it and keep it running, and nobody else writes the code.

02Krews Agent

Flagship product

AI operators that understand the company they work for.

Krews Agent is a managed AI operator built around one company: its SOPs, policies, products, tools and voice. It handles customer service, sales support, concierge, internal support, helpdesk and operations work. We build it, host it and keep tuning it.

One operator. Every layer behind it.

Behind the chat window sits your company’s knowledge, rules, memory, tools, retrieval and models, plus the monitoring and maintenance that keep them honest. We build all of it as one system.

Krews AgentManaged AI operatorOnline

Understands

  • Company profile
  • SOPs
  • Policies
  • Products
  • Knowledge base
  • Brand voice
  • Escalation rules
  • Internal tools
  • Workflows

Built, hosted and maintained by Krews

  1. L1InterfaceChannels
    • Website chat
    • Email
    • Helpdesk inbox
    • Internal chat
  2. L2ApplicationTools & rules
    • CRM and order actions
    • Escalation rules
    • Human handoff
    • Workflows
  3. L3IntelligenceModels & memory
    • Model routing
    • Conversation memory
    • Tuned adapters
    • Evaluations
  4. L4DataCompany knowledge
    • SOPs and policies
    • Products
    • Knowledge base
    • Retrieval index
  5. L5InfrastructureOperations
    • Private or shared inference
    • Tracing
    • Analytics
    • Maintenance

Assembled from your company.

A deployment in the order we build it: who you are, what you know, what must never go wrong, what it can do, and which models do the thinking.

  1. Company: It starts with who you are. Your profile, voice, markets and hours. The agent speaks as your company, not as a model with your logo on it.
  2. Knowledge: Then, what you know. SOPs, policies, product data and help content are ingested, structured and indexed for retrieval, then kept fresh as they change.
  3. Rules: Then, what it must never get wrong. Escalation thresholds, compliance limits and identity checks are enforced by the system, not suggested in a prompt.
  4. Tools: Then, what it can actually do. Scoped access to the systems your team uses. The agent resolves things instead of explaining how to contact support.
  5. AI: Then, the models, picked per task. Requests are routed to the right model for the job. Brand voice lives in a tuned adapter. Every release is gated by evaluations.
  6. Krews Agent: The result: an operator that knows where it works. One agent, assembled from your company, your knowledge, your rules and your tools. Deployed, monitored and maintained by Krews.

One request, end to end.

The agent classifies, pulls the relevant policy, calls your systems and checks its own rules before it answers. Every step is traced.

Illustrative runA damaged order, resolved without a queue.

  1. Customer, message: Customer. My order arrived damaged. Can I get a replacement before Friday?
  2. Krews Agent, classify: Intent. damaged_item · replacement · deadline: Fri
  3. Krews Agent, retrieve: Returns SOP §3. Damaged on arrival → free replacement, no return needed
  4. Company systems, tool call: orders.lookup. Delivered Tuesday · 1 item · verified customer
  5. Company systems, tool call: inventory.check. In stock
  6. Krews Agent, policy check: Escalation rules. Under review threshold · no human needed
  7. Company systems, tool call: shipping.reship. Express · arrives Thursday
  8. Resolution, resolve: Resolved. A replacement ships today and arrives Thursday. No need to send the damaged one back.

Deployment is the middle, not the end.

Agents drift when nobody looks after them. We stay on for tuning, monitoring and maintenance for as long as the agent works for you.

continuous

Discover

Learn the company it will work for.

We read your conversations, SOPs and systems with your team, and write down what a good resolution looks like before anything is built.

Krews handles

  • Use-case mapping
  • Conversation audits
  • SOP and policy review
  • Success criteria
  • Risk and compliance

03Services

Every layer.
One team.

Agentic AI leads, and most projects cross into the rest. Systems usually break at the boundaries between disciplines, so we’d rather own the boundaries too.

  • 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.

    Agentic AI & AI Systems

    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

    Works with vLLM · Open-weight models · Frontier model APIs · PostgreSQL + pgvector · Redis · OpenTelemetry · Python · TypeScript

04Engineering

Chosen for the system,
not the trend.

A short list of tools we know well, and a reason for every choice. These are the decisions we make most often, written the way we write them for clients.

Browserusers · crawlersCloudflare edgecache · WAF · DNSNext.jsprerendered pagesAPINode · TypeScriptModel routerper-task routingRetrievalhybrid · rerankPostgreSQL · Redispgvector · queuesOpen-weightvLLM · LoRAFrontier APIshard reasoningGPU nodesLinux · containersOpenTelemetry: one trace across every hop, including each agent step
A typical request path. Select any part to read the decision behind it.
ADR-04RetrievalAccepted

Hybrid search with a reranker, not vectors alone.

Context
Keyword search catches exact terms that embeddings blur, like SKUs, error codes and names. A reranker orders the combined candidates by actual relevance.
Trade-off
An extra model call per query. Cheap next to the cost of a confident wrong answer.
  • BM25
  • Vector retrieval
  • Rerankers

$ cat krews.sh/manifest

render
static HTML, every route
edge
Cloudflare Workers
css
inlined; long chapters skip layout until near
javascript
motion features load after first paint
components
shadcn/ui on Radix, restyled
fonts
self-hosted, subset
schema
Organization · Service · SoftwareApplication
tracking
none
motion
respects reduced-motion

This site runs on the same decisions.

05How we work

Understand. Build. Improve.
Repeat on purpose.

Seven stages, each ending in something you can open: a brief, a diagram, a commit, a pipeline run. The last stage feeds the first.

  1. Brief

    Problem

    Support volume doubles every season. Most questions are answered in the help centre already.

    Who it’s for

    Customers mid-order. Support staff at peak.

    Success looks like

    • Resolved without a ticket
    • Humans get the hard cases

    01 Understand

    Understand the problem before the solution.

    Conversations with the people involved, a look at the systems that exist, and a clear statement of what success means.

  2. Architecture
    webapiagentjobsragpostgres

    02 Plan

    Make the important decisions on paper.

    Architecture, scope, sequencing and cost, written down as decisions with their trade-offs.

  3. Interface

    03 Design

    Design the thing people will touch.

    Flows, interfaces and interaction details, prototyped early and tested against real content.

  4. Commits
    • a41f2c9featretrieval pipeline with reranker
    • 9be07d1testevaluation set for returns flow
    • 3c12e8afixcache key includes locale
    • e5d9b40perfdefer non-critical scripts
    • 71a0f3echoretyped client from schema

    04 Build

    Build it ourselves, in small verifiable steps.

    Typed, reviewed, tested code in a repository you own, with previews you can click on the whole way.

  5. Pipeline
    1. typecheckpassed
    2. testpassed
    3. buildpassed
    4. previewpassed
    5. productionrolling out

    05 Deploy

    Ship without ceremony.

    Automated pipelines, staged rollouts and a rollback that’s one command away.

  6. Signals
    LCP · p75 (illustrative)budget

    06 Measure

    Watch what actually happens.

    Field performance, errors, usage, search data and, for AI systems, traces and evaluations.

  7. Iteration
      // product pages
    - export const revalidate = 60
    + export const revalidate = false
    + // purged by tag when a product changes
    + revalidateTag(`product:${id}`)

    07 Improve

    Then make it better. Repeatedly.

    What we measure becomes the next plan. The systems we build keep getting maintained. That’s the point.

06Krews Lab

Where the next
systems start.

Agent architectures, internal tools and prototypes we run on ourselves before anyone else. The parts that hold up end up in Krews Agent and client work.

ProjectStatusExplorePrototypeBuildRelease
Internal toolEvaluation harnessRegression tests for agent behavior. Every production failure becomes a case that runs before the next release.In development
Agent architectureAgent memory runtimeConsolidation, decay and retrieval for agents that need to remember more than one conversation.Prototyping
Internal toolOperator consoleOne place to review conversations, traces and handoffs, and turn bad answers into test cases.Prototyping
Experimental AIAdaptive routingRouting that learns from evaluation results which model handles which request, and at what cost.Exploring
Possible productLog-based crawl analysisRaw server logs turned into a clear picture of how search engines actually spend time on a site.Exploring

Statuses are literal. Exploring means exploring.

07Work with Krews

Bring us
the hard part.

An agent for your team, a web product, a search or performance problem, infrastructure, or an idea that needs a plan. We’re early and small on purpose: the people you talk to are the people who build it.

  • 01A person reads it. No automated sequences.
  • 02We reply with questions, or a time to talk.
  • 03If we’re not the right team for it, we’ll say so.

Prefer email? hello@krews.sh

What do you need?