From conversational assistant
to the AI-augmented enterprise
A generative decision-intelligence platform: secure natural-language access to enterprise data and controlled automation of analytical tasks. An architecture that is sensitive to cost, connectivity, energy, local skills and data sovereignty.
The situation, plainly
Consumer AI showed what was possible. Inside an enterprise the problem is not the model: it is the distance between scattered data and a decision that must be taken today.
| What we observe | Consequence | What the platform provides |
|---|---|---|
| Scattered data: ERP, SQL databases, spreadsheets, business apps, CRM, logistics | Time lost consolidating | Federated access and a context layer |
| Dependence on IT or a handful of analysts | Bottlenecks | Natural-language querying |
| Indicators defined differently in every department | Contradictory figures | Semantic layer and a single KPI catalogue |
| Static reports produced on fixed dates | Little exploration, delayed decisions | GenBI, ad-hoc questions, interactive dashboards |
| Over-broad data access | Leak risk | Identity, entitlements, SQL guardrails |
| AI tools used outside any framework | Confidentiality risk | AI gateway, policies, audit logging |
| Weak integration between applications | Double data entry | APIs, agents, controlled automation |
The value chain: data to context to intelligence to action
Data
Federated sources, without bulk copying, with mapping, a business dictionary and quality rules.
Context
Semantic layer: shared definitions, KPI catalogue, calculation rules, business vocabulary.
Intelligence
Models and agents that turn a question into a guarded query, verify the result and explain it.
Action
Automation of repetitive analytical tasks, with human validation for anything critical.
Twelve control domains
Governance is not a chapter: it is a set of controls exercised continuously, verifiable and logged.
Identity
SSO, multi-factor authentication, enterprise directory.
Authorisation
RBAC/ABAC, row- and column-level security, scopes.
Data
Classification, masking, encryption, retention.
SQL
Blocking of writes and schema changes, allow-list, limits, timeouts, sandbox.
Models
Approved model list and routing rules by sensitivity.
External AI
Sensitive data blocked or filtered before any external call.
Prompts
Protection against instruction injection and misuse.
Audit
End-to-end logging and traceability.
Quality
Testing of answers, SQL and indicator accuracy.
Human
Mandatory validation of critical actions.
Cost
Quotas, budgets, alerts, consumption measured per department.
Compliance
AI policy, usage charter, named responsibilities.
A ten-step journey, fifteen to twenty-three weeks
Vision and scoping — 1 to 2 weeks
Use cases, governance, value objectives.
Data and systems audit — 1 to 2 weeks
Sources, quality, volumes, rights, infrastructure.
Target architecture — 1 to 2 weeks
Data, AI, security, integration.
Benchmark — 2 weeks
Candidate selection, scoring, comparative tests.
Proof of concept — 2 to 3 weeks
Representative cases on your data, measured performance.
Technology decision — 1 week
Decision report, total cost of ownership, detailed architecture.
Pilot — 3 to 4 weeks
Semantics, agents, pilot users.
Industrialisation — 2 to 4 weeks
Single sign-on, monitoring, security, backup, support.
Go-live — 1 to 2 weeks
Deployment, training, skills transfer.
Continuous improvement
New agents and indicators, model and cost optimisation.
The technology decision comes after requirements and a benchmark run on your data and your use cases — never before.
Sixteen contractual deliverables
| Phase | Deliverables |
|---|---|
| Scoping | L01 Scoping note, objectives and use cases · L02 Mapping of sources, flows and responsibilities · L03 Data catalogue and business dictionary · L04 Architecture dossier (data, AI, security, infrastructure) |
| Governance and decision | L05 AI governance policy and entitlement matrix · L06 Comparative technology matrix · L07 Benchmark report and justification of choices · L08 Working proof of concept |
| Build | L09 Semantic repository and KPI catalogue · L10 Business agents for the pilot scope · L11 Secured pilot platform · L12 Test plan and acceptance report |
| Production | L13 Operations, monitoring and backup dossier · L14 Administrator and user guides · L15 Training and skills transfer · L16 Go-live dossier and extension roadmap |
Infrastructure designed for Africa
Built from total cost of ownership and resilience, not technological fashion: bandwidth, energy, international cloud cost and local support are part of the scoping from day one.
On-premise
For sensitive data, when API costs are high and in-house skills exist: backed-up power, cooling, accelerators only after the proof of concept.
Regional private cloud
Elasticity and mutualisation, hosted under regional jurisdiction, for organisations without a server room.
Hybrid
Sensitive data kept locally, non-sensitive processing offloaded — with strict filtering of outbound data.
What the studies say — and what they do not say
Potential annual value of generative AI across 63 use cases — McKinsey Global Institute, 2023.
Potential contribution of AI to the global economy by 2030 — PwC.
Of the most advanced initiatives meet or exceed their expected return on investment — Deloitte, 2024.
Of African companies have access to digital tools, yet fewer than a third use them intensively — IFC, 2024.
Published orders of magnitude — these are not promises of return on investment for any given company: value depends on the use cases, data quality, integration and actual adoption. That is exactly why we start with a measured diagnostic.
Next step: a two-week scoping study, or a one-day workshop
The scoping study produces facts: prioritised use cases, real data, target architecture, cost estimate. The executive workshop produces a decision.