AI, wired into the work you already do.

Most teams don't need a moonshot. They need AI wired into the work they already do. We embed models, agents, and automation into your products and operations where they actually move the needle, not where they make a good demo.

Let's put AI to work

A pragmatic approach to AI

We start from the work you're trying to improve, not the model. Every integration is scoped around a real task, grounded in your data, measured against real examples, and shipped with the guardrails to keep it reliable.

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Prompt engineering & evaluation
We design, version, and test prompts against real examples, so outputs stay consistent instead of drifting when the model or your data changes.
Retrieval-augmented generation (RAG)
We ground answers in your own documents and data, so responses are specific to your business and can point back to their source.
Agents & tool use
We give models controlled access to your systems so they can complete real tasks, not just chat, with clear limits on what they can touch.
Guardrails & safety
Input and output checks, fallbacks, and human review where it counts keep AI accurate, on-brand, and safe to ship to customers.
Model selection & cost control
We match the right model to each task and tune for latency and spend, so you get the quality you need without overpaying per call.
A person working on a laptop

Built for what's next

AI that grows with you.

The systems we build aren't one-off experiments. Every integration is designed to adapt as models improve, your data grows, and new use cases emerge, so the AI you ship today keeps compounding in value tomorrow.

How it works

From first integration to long-term support.

  1. Discovery & scoping

    • Identifying the workflows and tasks where AI moves the needle
    • Assessing your data readiness and feasibility
    • Defining success metrics and a clear rollout plan

  2. Data & knowledge foundation

    • Preparing and structuring your documents and data
    • Building retrieval indexes and knowledge bases
    • Designing prompts and interaction patterns

  3. Model & pipeline build

    • Building RAG pipelines, agents, and tool integrations
    • Wiring models into your product and systems
    • Implementing access controls and fallbacks

  4. DevOps & LLMOps

    • Automating deployment and model rollout
    • Managing infrastructure and scaling in the cloud
    • Monitoring latency, cost, and system health

  5. Quality assurance

    • Running evaluations against real examples
    • Red-teaming for safety and accuracy
    • Enforcing guardrails and compliance

  6. Support & enhancement

    • Providing ongoing monitoring and updates
    • Gathering feedback to improve outputs
    • Adding capabilities as your needs grow
A support team collaborating at their desks

Case study

A support team that answers in seconds, not hours.

We built a RAG assistant grounded in a client's own help center and ticket history, wired into their support tool with guardrails and human review on edge cases. It now drafts the first response to most incoming tickets.

68%

of tickets drafted automatically

4x

faster first response time

0

hallucinated policy answers in review

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What clients say

Anksys wired AI into our support flow without breaking anything we already relied on. It handles the routine questions, and our team finally has time for the hard ones.

Al Habib

Head of Support, SaaS

Anksys engineers working through a problem together

Tell us where it should plug in

Ready to put AI to work?