> ai.strategy.architecture
Enterprise AI Strategy. Engineered For Real ROI.
We diagnose operational bottlenecks, architect private document retrieval (RAG) pipelines, and deploy compliant AI systems that produce verified ROI without leaking sensitive enterprise data.
> first pilot live in 2–4 weeks, with evals and ndpa compliance from day one
> strategic.packages
Defined Scope. Transparent Fixed Pricing.
We tell you upfront where AI delivers measurable leverage and where simple code is the better answer. No open-ended consulting bills.
> diagnostic.blueprint
10 Business DaysAI Diagnostic & Architecture Blueprint
₦450,000
A rigorous technical and workflow diagnostic of your business operations. We identify where AI creates genuine leverage and where simple, deterministic code or database triggers are the superior solution.
- ✓ Process bottleneck mapping & time-study review
- ✓ Model selection & token budget analysis (hedging dollar APIs)
- ✓ NDPA 2023 compliance & data residency audit
- ✓ Production architecture blueprint & technical specification
- ✓ Implementation roadmap with estimated payback window
> rag.agents.build
3–4 WeeksCustom Enterprise RAG & Agents
From ₦1,850,000
Private document retrieval and custom tool-calling agents grounded exclusively in your internal knowledge, verified with regression evaluation suites before shipping to production.
- ✓ Private document ingestion (PDFs, policies, catalogues)
- ✓ Hallucination-resistant retrieval with explicit source citations
- ✓ Tool-calling agents with strict human-in-the-loop approvals
- ✓ Automated evaluation suites for quality assurance
- ✓ 100% IP & infrastructure handover to your cloud environment
> local.engineering.reality
Engineering For The Nigerian Operating Environment.
Building AI systems in Nigeria requires answering questions global agencies skip: foreign exchange volatility, data protection law, and reliable offline-tolerant fallbacks.
> fx.insulation
Dollar API Cost Insulation
Model APIs bill in US Dollars while Nigerian businesses earn in Naira. We engineer aggressive semantic caching, prompt pruning, and local model fallbacks so exchange rate spikes never blow up your monthly operating budget.
> ndpa.compliance
NDPA 2023 & Data Sovereignty
Customer phone numbers, national IDs, and financial records must comply with the Nigeria Data Protection Act 2023. We ensure sensitive business data is never transmitted to public model training loops or unsecured overseas endpoints.
> zero.hallucination.money
Deterministic Ledgers for Money
We never permit an LLM to calculate account balances, approve payment amounts, or issue invoices autonomously. Financial math and identity lookups run strictly in deterministic code.
> the.models
Fluent Across The Whole Model Landscape.
We are not married to one provider. Frontier APIs for hard reasoning, open-weight models self-hosted for cost and privacy - routed per task and measured per outcome.
OpenAI
Anthropic
Google Gemini
Mistral
Meta Llama
Hugging Face
Ollama
LangChain
> what.we.build
Six Ways AI Earns Its Keep.
> 01
AI Agents
Agents that take actions, not just answer questions - reading records, drafting replies, updating systems and escalating to a human when the stakes demand it.
- ✓ Tool-calling & function use
- ✓ Multi-step task execution
- ✓ Human-in-the-loop approvals
- ✓ Full action audit trail
> 02
Retrieval (RAG)
Answers grounded in your own documents - policies, catalogues, contracts, knowledge bases - with citations, so the model speaks from your facts, not its imagination.
- ✓ Document ingestion pipelines
- ✓ Vector & hybrid search
- ✓ Cited, verifiable answers
- ✓ Freshness & re-indexing
> 03
Model Strategy
The right model for each job, not the most expensive one for every job. Frontier models where reasoning matters, open-weight models where cost or privacy does.
- ✓ Model routing & fallbacks
- ✓ Open-weight self-hosting
- ✓ Cost per task budgets
- ✓ Latency engineering
> 04
Evaluation & Safety
Every AI feature ships with a scored test suite. Prompts and models change; the eval suite is what tells you the system still behaves before your customers find out.
- ✓ Regression eval suites
- ✓ Hallucination guardrails
- ✓ Spend & scope limits
- ✓ Red-team scenarios
> 05
Conversational Interfaces
AI where your customers already are - WhatsApp, Telegram, Slack, web chat and voice - with resumable flows, receipts and a clean handoff to a human.
- ✓ WhatsApp & Telegram bots
- ✓ Slack assistants
- ✓ Web chat & voice
- ✓ Human handoff with context
> 06
AI Operations
Someone accountable after launch: monitoring every run, tracking token spend, reviewing failures weekly and tuning monthly against the numbers we agreed.
- ✓ Run monitoring & alerting
- ✓ Token & cost tracking
- ✓ Failure review cadence
- ✓ Monthly tuning reports
> the.surfaces
Deployed Where Work Already Happens.
AI is only useful where people actually meet it. We ship into the channels, sheets, databases and clouds you already run.
Telegram
Slack
n8n
Make
Zapier
Google Sheets
Airtable
PostgreSQL
Docker
Cloudflare
AWS
> how.we.think
Principles Before Prompts.
Deterministic where it matters
Money, identity and stock never depend on a model's mood. Anything that must be right every time is code; the model handles language, judgment and routing.
Measured, not vibes
Every AI feature ships with a scored eval suite and a cost budget. If a prompt or model change makes things worse, we know before your customers do.
A human can always say no
Refunds, deletions, public posts and anything irreversible pass through an approval gate. The agent prepares; a person decides.
You own everything
Prompts, pipelines, evals and infrastructure are documented and handed over. If we part ways, your AI keeps running.
> how.it.runs
From Audit To Operating System.
01
Opportunity audit
We map where AI genuinely saves hours or money in your operation - and where it is a science project you should skip.
02
Pilot with evals
One use case, shipped end to end with a scored test suite and an approval gate, measured against the manual baseline.
03
Harden and extend
Guardrails, monitoring and cost controls go in; then the next use cases roll out on the same foundation.
04
Operate and tune
Weekly failure reviews, monthly tuning, model upgrades when they earn their place - with a report you can actually read.
> questions
Straight Answers.
Which AI models do you use?
Whichever earns its place per task. Frontier models from OpenAI, Anthropic and Google for hard reasoning; open-weight models like Llama and Mistral - self-hosted via Ollama or Hugging Face - where cost, volume or data privacy makes them the better answer. We route between them and measure the difference.
Can AI work with our private company data?
Yes. Retrieval pipelines ground answers in your own documents with citations, and sensitive workloads can run on self-hosted open-weight models so data never leaves your infrastructure.
How do you stop it making things up?
Grounding in your content, constrained tool sets, deterministic code for anything involving money or identity, and a scored eval suite re-run whenever prompts or models change. The model is never the last word on anything irreversible.
What does an AI pilot cost in time?
A scoped pilot is typically live within two to four weeks. We deliberately start with one measurable workflow rather than a platform, so value shows up in numbers, not slides.
> start.with.one.use.case
Put AI On The Expensive Problem First.
Tell us where the hours go. We will tell you honestly whether AI removes them - or whether a simpler fix does.