Solutions
AI that runs inside real operations, on real data, with accountable humans in the loop.
01Overview
Most AI projects stall between the notebook and the workflow. The model works on a sample file, then meets live data, missing fields, slow approvals and a team that was never asked what it needed. We work in that gap. Our engineers sit with the people who make the decision today, and we build the AI around that decision rather than around a model.
We pick use cases by looking for a repeated decision with real cost: which loan application needs a second look, which shipment will miss its window, which patient record is incomplete, which invoice is out of pattern. Each one is built as a small system with data in, a recommendation or action out, a named person who owns the outcome, and a record of what happened.
We stay until the system is measured, trusted and owned by your team. That means test sets built from your own data, monitoring that tells you when quality drifts, and runbooks written for your operators, not ours. Where it fits, the AI acts on the Boltcliq ontology so that every agent shares one definition of a customer, an asset or a case. Where it does not, we build on the stack you already have.
02
Applied AI is not a product we install. It is a way of working. In the first weeks we sit beside the analysts, officers and operators who make the decision, watch how they do it and write down every exception. That list of exceptions is the real specification.
Then we build in small steps and put each step in front of the same people. A recommendation that arrives inside the tool they already use, with the evidence beside it, gets used. A score on a separate screen does not.
03What you get
We build models and rules that recommend, rank and flag on live operational data. Each system is scoped to one decision, such as credit review, claim triage, stock replenishment or case prioritisation. Output arrives with the inputs that drove it, so a reviewer can agree or overrule in seconds.
A recommendation people can check is a recommendation people use.
Agents handle multi-step work such as gathering documents, drafting a reply, reconciling two records or preparing a file for approval. Each agent runs with its own identity, a short list of permitted actions and a human approval step wherever money, health or rights are affected. Agents stop and ask when they are unsure.
Autonomy is earned in steps, with limits you can see.
Many African organizations run on scanned forms, handwritten registers, WhatsApp messages and mixed-language text. We build extraction and understanding pipelines for those formats, and test them on the real documents and languages your customers use. Quality is measured per field, not as one headline number.
The data that matters is often not in a database yet.
We write the tests before the system goes live. That includes accuracy on your own labelled cases, behaviour on hard and adversarial inputs, bias checks across the groups you serve and cost per decision. After launch, monitoring tracks drift and routes failures to a person.
AI that cannot be tested cannot be signed off.
We design the screens, queues and escalation rules that connect people and AI. That covers who sees what, how long they have, what happens when nobody responds and how a correction flows back to the system. The goal is a team that feels more capable, not more watched.
Adoption is a design problem as much as a model problem.
We write runbooks, train the operators and leave dashboards that show quality, cost and usage. Your engineers take over deployments, evaluations and retraining. We stay on call for a defined period and then step back.
The real test is the month after we leave.
Free 30 minutes with an engineer.
04African sectors
Situation
Credit officers review loan files that mix formal statements, mobile money history and handwritten notes from field agents. Reviews are slow and uneven.
What it does
A decision system assembles the file, extracts figures from scans, flags gaps and proposes a risk band with the evidence behind it.
Human decides
The credit officer approves, declines or asks for more information, and records the reason.
05Boltcliq platform
Boltcliq product
Gives agents one shared model of customers, accounts, cases and documents, so a recommendation refers to the same objects the business uses.
Agents stop guessing what a field means.
Boltcliq product
Controls what each agent may load, remember and do, and holds approval rules for sensitive actions.
Limits are enforced, not just documented.
Boltcliq product
Records decisions, costs and quality over time, and shows when a model drifts or an agent fails.
You can prove what the AI did and how well.
06
Does
Does not
07FAQ
Experience you can lean on
Immersed in technology and across many different verticals, so the people on your project have done this before.
Applied AI
Tell us the problem. An engineer, not a salesperson, answers.
AI that runs inside real operations, on real data, with accountable humans in the loop.
