AI Integrations
We add AI where it removes real, repetitive work, grounded in your own data, with evaluation before launch and human review where mistakes are costly.
See this in practice
Roaming Audit
Partner management, launch coordination, IR.21 management, configuration audits and security checks for Tier-1 mobile operators, with an AI assistant grounded in GSMA specifications and the operator's own data.
Problems we solve
Answers are buried in documents, specifications and records.
What we build: Search and question answering over your own knowledge (RAG), with sources shown for every answer.
People copy data out of documents by hand.
What we build: Document extraction that pulls the fields you need and flags low-confidence results for review.
Support teams repeat the same replies.
What we build: Suggested answers drafted from your knowledge base for a person to approve.
Hand-offs between systems wait on people.
What we build: Workflow automation that moves checked data between the tools you already use.
How the engagement works
01
Check the use case, the data you can access, and whether AI is the right tool.
02
A working version on real examples, measured against agreed accuracy.
03
Connect it to your systems, with human review where it's needed.
04
Track quality and running costs in production, and improve over time.
Turning an idea or a problem into a clear plan before committing to a build.
A short, fixed-scope phase of workshops, research and technical planning with your team.
You get
Getting a first release in front of real users.
A defined first release, designed, built and launched against an agreed scope.
You get
Products that need steady improvement after launch.
A dedicated Corexlab team working through your roadmap, with priorities agreed each cycle.
You get
Technical capabilities
Cost and timeline
Data quality and access
Where the data lives, how clean it is, and how we're allowed to use it.
Accuracy required
How good the results need to be, and how we evaluate them before launch.
Volume
How many documents or requests, which sets the monthly model running costs.
Integrations
Which systems the AI reads from and writes back to.
Human review
Where people approve results, and the workflow they use to do it.
A scoping call turns these into a real estimate for your project.
FAQ
We focus on integrating proven models, such as OpenAI's, with your data through retrieval and careful prompting. That covers most business use cases without the cost of training a model.
We agree up front what data the AI can use and where it is processed, and sign an NDA before we see any of it.
Running costs depend mostly on volume. We estimate them during the assessment and track them after launch.
Then we'll say so. The assessment exists to find that out before you invest in a build.
Further reading
AI engineering
A demo that works once is not a feature. How we build eval sets, regression gates and monitoring so an AI capability behaves the same way on day 90 as it did on day one.
6 min read
AI engineering
What goes in, what comes out, and what it costs. A fixed-price way to find out whether one workflow is worth automating before you commit to a build.
5 min read
Share where things stand and what you need. We'll come back with questions and a suggested next step.