Service

AI integration for work that has a clear reason to be automated.

KomodoWorks helps organisations assess and build practical AI features without treating AI as the answer to every problem. Suitable work starts with a defined task, reliable source material, clear limits, and a person responsible for reviewing important output.

A calm night-time business environment with illuminated work areas and connected operational spaces.

Who it is for

A useful fit when

  • Teams repeatedly searching the same internal documents or guidance
  • Businesses handling high volumes of routine classification or drafting work
  • Organisations exploring a customer or staff assistant with controlled source material
  • Teams that need to test whether an AI workflow is useful before committing to a larger build

Problems addressed

What the work can change

  • Staff spend time finding and reshaping information that already exists
  • An AI idea has no agreed source material, evaluation method, or human review point
  • A prototype works in a demonstration but is unreliable in normal use
  • The organisation needs to understand risk, data handling, and operational ownership before launch

What we deliver

Work defined around the outcome

  1. 01Suitability reviews that identify where AI is useful and where deterministic software is safer
  2. 02Focused prototypes tested against realistic examples and failure cases
  3. 03Document-based assistants, classification tools, and supported drafting workflows
  4. 04Evaluation criteria, guardrails, source handling, and human-review steps
  5. 05Documentation, training, launch support, and handover

How the engagement works

Clear decisions before each stage moves forward

01

Define the task and risk

We identify the users, source information, expected output, unacceptable failures, sensitive data, and decisions that must remain with a person.

02

Test a constrained approach

A focused prototype is evaluated against realistic examples before the project expands into production work.

03

Build the surrounding controls

The feature includes the validation, permissions, source references, monitoring, and review steps required for its actual use.

04

Document and introduce

Users receive clear guidance about what the feature does, what it does not verify, and when output needs human review.

Relevant experience

Experience with applied and evaluated AI

Kagan Timur designed and architected an EU AI Act compliance chatbot during a one-year Data Science internship at Orcawise. He also has professional experience in generative AI annotation and evaluation at Covalen.

Read about KomodoWorks

Pricing context

AI work is scoped around the task and its safeguards

Price depends on source material, integrations, evaluation work, data sensitivity, user access, and the controls needed around generated output. A small suitability review or prototype can precede a production build.

Questions about this service

What business tasks are suitable for AI automation?
Good candidates are repeated tasks with clear inputs, a defined useful output, enough representative examples, and a practical review process. High-impact decisions, unclear source material, or tasks requiring guaranteed correctness need stronger controls or a different approach.
Do we need an AI solution for every automation project?
No. Rules-based automation is often more reliable and easier to maintain. The suitability review compares AI with simpler software before a technical direction is agreed.
Can an assistant answer from our own documents?
Yes, when the documents can be prepared, permissioned, and kept current. The design should show source references, limit unsupported answers, and make ownership of the content clear.
How do you test an AI feature?
We define representative tasks, expected qualities, unacceptable failures, and review criteria. Testing includes normal examples, ambiguous requests, missing information, adversarial input, and the situations where the feature should decline or defer to a person.
Will people remain involved in the workflow?
Yes, where the task or consequence requires it. The project defines which output is reviewed, who reviews it, and what happens when the system is uncertain or unavailable.

Start with the task, not the model.

Tell us what people do today, which information they use, and what a useful result would look like. We will assess whether AI belongs in the solution.

Discuss an AI workflow