Reads your data, drafts the next step
AI Agent Design & Automation in Tanzania
An agent is worth building where someone’s job is reading data and acting on it: flagging exceptions, drafting follow-ups.
01 — The problem
Signs you need this
- 01.1
Someone scans the same data every morning for exceptions
- 01.2
Follow-ups slip because chasing them is nobody’s job
- 01.3
Someone writes the same weekly summary by hand
02 — Deliverables
What you get
- 02.1
Agents scoped to a real task
Built from work someone does today. If it needs no agent, we say so.
- 02.2
Grounded in your own data
Every output can be checked against your own systems.
- 02.3
A human in the loop where it matters
Anything consequential waits for approval; you decide where that line sits.
- 02.4
Built on the same secure foundation
Agents follow the same access rules as your staff.
03 — Sample
See it in a sample
Charts from our sample dashboards, with the decision each one supports.
Sample data — we never publish client dashboards
Sample lender’s reporting system · September 2026 · schedule for a sample Monday · TZS
SAMPLE DATA
What changed this week
Written by the reporting agent · sample
- Mbeya PAR30 rose 0.8 points to 7.2%, mostly from two loan officers.
- Dodoma disbursements are 18% below plan, mostly in group loans.
- September’s TZS 2.30 bn is the year’s first month above target.
Agent log
The agent drafts. A person approves anything that leaves the building.
- 05:30Daily PAR report sent to 14 branch managers. Sent
- 05:58Monday board pack sent to 9 recipients. Sent
- 07:02PAR30 above the 5.0% limit in Mbeya (7.2%) and Dodoma (5.4%). Note drafted for the Head of Credit. Awaiting approval
- 07:09The Head of Credit approved the note; sent to both branch managers. Sent
- 07:1541 arrears reminders drafted for loan officers to review. Queued for review
- The agent drafted the note; a person approved it seven minutes later.Decision: You choose which steps stay with a person.
Source: DataHub sample data · TZS · Sep 2026
Every name and number here is invented.
Source: DataHub sample data · TZS · Sep 2026
04 — Questions
Questions about AI Agents
Q 04.1Is this useful for a Tanzanian business, or only large companies?
It depends on the task, not company size: an hour a day spent reading data and reacting is worth automating.
Q 04.2Will an AI agent make decisions without us?
Only where you decide. Most of our agents draft and a person approves; full automation is for low-risk steps.
Q 04.3What does an agent actually cost to run?
A build cost, then a running cost that depends on the data it processes. We size both before you commit.
05 — More services
