One platform for your internal data, the world's data, and the AI that connects them.
Three things surfaced from your data and the world's overnight.
Connect every source. Set the rules. Certify what passes. One catalog for everything. Share access, not files.
Databases, apps, spreadsheets, file drops, live feeds. Bring them all into one place, in whatever shape they come in.
Set the rules your data must meet: quality rules, governance rules, privacy rules. Griot turns your definitions into a data contract, and only data that meets the contract gets in.
Once data meets its contract, we sign it and give it a trust score. A clear, checkable mark that this data is sound to be used. Anyone can verify it.
Subscribe to live external datasets like consumer prices, FMCG benchmarks, footfall and weather. Query them right next to your own POS, loyalty and inventory tables. The platform contracts the outside feed the same way it contracts your internal data, so trust, lineage and access all carry through.
Give a partner access to your data and insights without handing over a data copy. They query it, they never hold it, and you can revoke access any time.
Relational databases, business apps, spreadsheets, file drops, live feeds. Plug them in once and start querying.
Sales transactions · 1.24M rows · owned by Susan Wairimu · last published 14 May 2026
Rows must clear these checks before they reach the catalog.
How sensitive fields are protected before they leave the contract boundary.
Who can read, who can write, how long it lives, where it stays.
Three steps, in order. Each one has to pass before the next can start. The platform signs the result the moment they all do.
Your internal retail datasets alongside outside data that pairs well with them. Same query surface, same contracts, same audit log.
Behavioural cohorts for the loyalty programme. Basket value, repeat rate, channel mix. Built from your POS and app feeds, refreshed weekly.
Monthly CPI across 13 retail categories. Food, household, apparel, electronics. Join against your SKU prices to model elasticity and reshape promo calendars.
Each partner queries the retail datasets you granted them. Nothing copies, nothing leaves. You can switch any of this off in a click.
14:02 EAT · queried orders_for_dispatch for the Nairobi delivery zone. 1,284 parcels routed. No customer PII returned.
13:47 EAT · joint promo-uplift study on the household-essentials SKU range. 47 stores compared against control basket. Lift confirmed +12.4%.
Your tables are contracted, certified and scored. Deploy AI right alongside them, on the same platform.
Findings Griot surfaced from your contracted data while you were away.
3 mobile-wallet transfers to Mauritius were quarantined at 04:18 UTC by the sanctions-screening pipeline. Root cause: Mauritius was added to the cross_border_transfer allowlist on 4 May but the contract update (v2.5.0) has not yet propagated to all replicas.
Sentinel-AML processed 312 alerts in the last overnight run and flagged 23 high-risk patterns for human review. That is a 12.4% week-on-week increase. Elevated origination from the Eastleigh sub-network and three new counter-party names not previously seen in the ledger.
The credit-decisioning model detected a 2.3σ drift in the Q2 default-rate cohort relative to Q1. The 30-day probability-of-default distribution has shifted upward for applicants aged 25-34 in Nairobi Central and Mombasa.
Disbursements in Mombasa fell from a KES 142M weekly run-rate to KES 122M between 28 Apr and 5 May, a 14.1% drop. The decline is concentrated in three branches: Mombasa Nyali, Mombasa Mtwapa, and Mombasa Kongowea, together accounting for 71% of the gap.
Auto-decline rates on pl_credit_decisioning rose 18% over the same window. The credit-decisioning model lifted its approval threshold from 65% to 72% PD after the ct_credit_decisioning_ai v3.1 recalibration on 24 Apr, which itself was triggered by the loan-default-cohort drift insight that morning.
Net: the model is working as governed; the dip is policy-driven, not pipeline-driven. The three Mombasa branches index higher on the affected applicant cohort.
Each one earns its trust on four things. Three cleared. Two didn't. The agent won't pretend.
"I'd rather wait than write a Q2 summary on stale prices and 18-hour-old FX rates. Refresh products and currency_rates and I'll have the draft on your desk in twelve minutes. Or tell me to use them anyway and I'll add a footnote noting the gap."
Drafts the daily and quarterly sales reports. Answers questions about revenue, pipeline, customer cohorts. Reads only what its policy says it can.
Plain rules per column, not per table. Names are tokenised. Pay data isn't even in the room.
Plug in Claude, ChatGPT, Gemini, an open-source model, or one you trained yourself. Each one runs through the same contracts, the same privacy rules, the same audit log.
Proactive analysis, always on. Three things waiting for you when you open the app: something unusual, something shifting, something that needs attention. Each one points to the data behind it.
Ask the way you'd ask a data analyst. Every answer shows you the exact data it used, so you can check it yourself.
Every dataset has a trust score. If the data isn't good enough, the AI won't use it. It tells you instead of guessing.
You decide what each agent can see and do. Private data stays private. Every action it takes is logged.
Plug in OpenAI, Anthropic, Gemini or your own fine-tuned model, and run it on your contracted data inside your perimeter. The model comes to your data, not the other way around.
Griot runs where the law says it has to. Use Griot on your servers, local datacentres, or the public cloud.
Ready to see it on your own data?
