Credit analysis · SMEIllustrative
Turnover has fallen for two quarters; the collateral has not been revalued.
→ revalue the collateral
Financial institutions · Industry · Services
AI for work that takes expertise.
It knows your field, and works in your systems, on your rules.
Your experts still make the call.
AI is not one thing. Three parts, and how they work together.
Machine-learning model. Estimates and groups: turns raw data into a score and a segment.
Small language model, trained for your domain. Trained for your domain and task; reads the score, the segment and the incoming document like your expert.
The harness around the model. Memory, tools, a log, a sandbox, and the limit you write. Model plus harness is the agent.
Agentic AI system. Finds the right next steps; acts inside the limit through your systems; the rest goes to your expert.
Your expert has the last word. Each part works on its own; together where the work needs it.
Start with a Decision Blueprint.
Press 3 · vibrationIllustrative
→ seal wearing; plan a stop
Call · 7 minIllustrative
Customer: A fee was taken from my card. Agent: I can offer a refund.
→ eligibility not checked
Solutions
The work in your business that takes expertise. Pick one and see what we build there.
Financial institutions & fintech26 use cases
What we build here
What we build here
What we build here
What we build here
What we build here
What we build here
Industry4 use cases
What we build here
What we build here
What we build here
Services3 use cases
What we build here
What we build here
These use cases describe systems we build, based on how these desks really work. The people, institutions, and amounts in the documents are illustrative; we use no real customer data. We show it on your data, in your environment, in the Decision Blueprint.
How expertise transfers
Your expert writes the test. The model goes live only on the work it passes.
Test first
Your expert grades real past work, before any training.
Train on your work
The model learns from work your team already decided.
Same test again
The same work, the same grader.
Live where it passed
The rest stays with your experts.
What we build
Three kinds of AI; each works on its own.
- Machine-learning modelsTurns what your expert has seen for years into numbers: scores, probabilities, forecasts, and the action your policy writes for each band.Read more
- Small language models trained for your fieldKnows your field's language: reads like your expert, calls the right tool, and writes in your format. The model stays yours.Read more
- Agentic AI systemsDoes your expert's preparation: gathers the evidence and prepares the work, acting only inside your written limits.Read more
All of it connects to the systems you already run: core banking, ERP and MES, CRM and the contact center, your decision engine. What it connects to
How we start
It starts with one piece of work and is handed over running.
Decision Blueprint
Typically 2–4 weeks
A fixed fee, agreed before it starts; it ends in a written plan.
Validation build
Typically 4–8 weeks
Measured on your past work, with your expert's test.
Production, integration, handover
Typically 3–6 months
Goes live only on the work it passed.
- The model, its weights, and your data stay with you; base-model licenses still apply.
- A senior engineer leads every engagement.
- Everything is documented, so the system never depends on one person.
We come into your environment, build beside your team, and hand the system over running.
About eqlxEqualize the gap.
Bring work that takes expertise. Leave knowing the first step.
A short description of the work is enough; no confidential records.
https://eqlx.ai · Printed copy; the website is the current version.