Financial institutions · Manufacturing · Services
Equalize the gap.
Your decisions wait on files people rebuild by hand.
eqlx builds decision systems for financial institutions, manufacturers, and service companies: machine-learning models that estimate, small language models trained for your field and task that read, reason, and call your tools, and agentic AI systems that prepare each case for the person who signs. They run in your environment and are handed over running.
TodayCalls reach customers who would have paid anyway, while the ones who cannot keep up need a plan, not ten calls.
With the systemHardship told apart from won't-pay, with the plan's arithmetic on file.
- EstimateChance it pays on its own: low. Your policy sends this band to a call and a plan.
- UnderstandReads the call notes and drafts a payment plan from your policy's menu.
- Prepare and actAdds it to today's call list at an approved hour and records the promise.
Collector approves the payment planEvery restructuring offer is a person's; above the threshold, a supervisor's.
TodayThe press fails on the night shift; its vibration had changed weeks before.
With the systemRising stop risk flagged, with a draft work order and a proposed slot.
- EstimateRisk of an unplanned stop next week: high. The vibration pattern changed.
- UnderstandReads the maintenance log and the manual, and finds the repair that fixed it before.
- Prepare and actReserves the seal kit and drafts the work order for the planned changeover.
Maintenance planner releases the work orderEvery stop and every work order is the planner's; the agent drafts and reserves only.
TodayEvery call is recorded, quality hears two in a hundred, and off-the-shelf speech-to-text mangles your product names.
With the systemEvery call checked for quality and compliance; the coaching point in context.
- Estimatenot needed on this desk
- UnderstandTranscribes the call in your terms and scores it on your scorecard: a refund offered without the eligibility check.
- Prepare and actOpens an exception ticket and drafts a coaching note with the moment in the call.
Team lead decides the scoreThe score is advisory; every score that affects a person's review is a person's.
What we build
Three kinds of AI. One decision system.
Each layer works on its own; they stack where the decision needs more.
EstimateMachine-learning models
- Score
- a risk score with its two main reasons
- Detect
- one unusual payment flagged
- Forecast
- demand rising over the next quarter
- Optimize
- the best plan inside your limits
- Segment
- customers sorted into a few groups
- Rank
- the offer most likely to fit, first
Numbers your team can explain, defend, and monitor.
Machine-learning modelsUnderstandSmall language models · trained for your field and task
- Your field
- tells a loan covenant from an ordinary condition
- Reasoning
- works the task step by step and shows why
- Tool calls
- the right tool with the right parameters, such as a SQL query or a JSON request
- Case memory
- keeps what the case needs across its steps
- Your format
- laid out in your own template
- A new skill
- passes your expert's test before it goes live
Trained for one field and task, a small model often does a large general model's work there, and can be the model an agentic system reasons with.
Small language modelsPrepare and actAgentic AI systems
- Tools and access
- three tools, all read-only
- Safety checks
- an unsafe instruction blocked
- Memory and sources
- every fact with the page it came from
- Way of working
- a fixed workflow, one step after another
- A person decides
- the file goes to the credit manager
- Tested and recorded
- every step kept on the record
The work around a decision, done inside your rules.
Agentic AI systems
Integration · the base
- Core banking
- reads the customer's exposure
- ERP and MES
- drafts the work order
- CRM and contact center
- adds the note to the case
- Your decision engine
- your engine still decides
- Documents and data
- links the file's documents
- Your servers or cloud
- runs where you choose
Under every kind: into the systems you already run, legacy ones included, connected and left unchanged.
What it connects toIllustrative
How it earns trust
Your expert writes the test. The model goes live only on the tasks it passes.
Models are tested on past cases they never saw in training. Small language models learn from files your team already decided. Agentic AI systems stop where your written limits say.
1Test first
Real past cases, graded by your expert before any training.
2Train on your files
Training examples and practice rounds built from cases your team already decided.
3Same test again
The same cases, the same grader.
4Live where it passed
The rest stays with your people.
How models are tested How small models learn your task How agentic systems stay inside limits
Financial institutions & fintech
14 use cases
Manufacturing
4 use cases
Services
3 use cases
These use cases describe systems we build, written from how these desks work. They are not past client projects: no client names, no results.
How we work
A senior engineer leads every engagement.
From the first question to handover. Everything is documented and handed over, so the system never depends on one person.
How to start
1Decision Blueprint
Typically 2–4 weeks · A fixed fee, agreed before it starts.
If the plan says build: a validation build, then production, integration, handover.
We come into your environment, build beside your team, and hand the system over running.
Equalize the gap.
Bring one decision. Leave knowing the first step.
A short description of the decision is enough; no confidential records.
https://eqlx.ai · Printed copy; the website is the current version.