Services / AI Development
AI built for the
problems you actually have.
Custom machine learning, LLM integration, and agentic AI — deployed on your infrastructure, measured against real metrics, built to survive production.
What we build
20+
AI systems shipped
5
Ghanaian languages
99.9%
Model uptime
3 wks
Prototype to demo
AI Readiness
Is your organisation
ready for AI?
Six questions. No email required. You'll get an honest read on where you stand — and what to do next.
Most organisations that fail with AI skip this step. If you can't answer these honestly, no vendor can help — no matter how good the model.
01Do you have clean, accessible data for the problem you want to solve?
02Do you have historical examples or labelled outcomes?
03Do you have a specific business problem in mind?
04Who owns AI decisions in your organisation?
05Have you defined what success looks like?
06Is your infrastructure ready for AI workloads?
0 of 6 answered
Industry solutions
AI shaped by your
industry challenges.
Eight industries, each with its own AI blueprint — not generic templates.
AI for GovTech
- Citizen service virtual assistant
- Document processing & verification
- Tax fraud detection
- Policy compliance monitoring
AI for BFSI
- Credit risk scoring
- Transaction anomaly detection
- Loan underwriting
- Wealth advisory
AI for Healthcare
- Clinical triage support
- Medical document extraction
- Diagnostic assist
- Patient engagement
AI
Solutions
Purpose-built for each sector. Same standard everywhere.
AI for Education
- Admissions ranking
- Automated grading
- Personalised tutoring
- Performance prediction
AI for Agriculture
- Crop disease detection
- Yield forecasting
- Farmer advisory
- Weather analytics
AI for Retail
- Recommendation engines
- Demand forecasting
- Price optimisation
- Visual search
AI for Logistics
- Route optimisation
- Predictive fleet maintenance
- Demand forecasting
- Driver safety analytics
AI for Digital Natives
- Hyper-personalisation
- LLM-powered search
- Onboarding assistant
- Automated QA
Capabilities
Full-stack AI.
Nothing missing.
From classical machine learning to LLM agents — every layer of the modern AI stack.
Talk to engineeringCustom ML Models
Fraud, churn, credit risk, demand — trained on your data.
LLM Integration
GPT-4, Claude, Llama wired into your product with guardrails.
RAG Systems
Chat with your documents. Grounded answers, source citations.
AI Agents
Multi-step autonomous workflows that complete tasks end-to-end.
Document Intelligence
OCR, extraction, classification at thousands of docs per hour.
Predictive Analytics
Forecasting models that learn from your historical data.
Computer Vision
KYC verification, medical imaging, quality control, retail analytics.
Speech & Voice AI
Transcription, IVR, voice assistants — in local languages.
Our AI stack
The technology we master.
Right tools for the right job — from foundation models to infrastructure, orchestration, and MLOps.
Cloud & Infrastructure
AWS
Azure
Kubernetes
Docker
Terraform
DigitalOcean
ML & Data Science
Python
PyTorch
Pandas
Jupyter
LLM & Model Providers
OpenAI GPT-4
Anthropic Claude
Google Gemini
Meta Llama
Mistral
Open-Source SLMs
Data & Storage
PostgreSQL
MongoDB
Redis
Elasticsearch
Custom open-source LLMs
Your data.
Your model.
Your rules.
We fine-tune open-source models — Llama, Mistral, Qwen, and others — on your corporate data. The result: a proprietary model that speaks your domain, runs on your infrastructure, and never sends a single token to a third party.
The fine-tuning pipeline
Base model selection
We pick the right open-source foundation for your use case — 7B for speed, 70B for reasoning, MoE for balance.
Data preparation
Your documents, tickets, logs, and conversations cleaned, deduplicated, and formatted for training.
Fine-tuning
LoRA, QLoRA, or full fine-tuning — depending on budget, latency needs, and accuracy targets.
Evaluation
Held-out test sets, domain-specific benchmarks, and human review. We publish honest accuracy numbers.
Deployment
Served on your GPUs — vLLM, TGI, or Triton. Scaled horizontally. Zero external API calls.
Continuous improvement
New data feeds back into periodic retraining. The model gets better the longer it runs.
Internal document Q&A
Ask questions across contracts, policies, case files, and internal wikis.
Customer service copilot
Agent-assist that knows your products, pricing, and escalation rules.
Code assistants
Trained on your codebase conventions, patterns, and internal libraries.
Legal & compliance research
Grounded answers over regulatory filings and precedent documents.
How we compare
Decision points that matter.
Decision point
Typical vendor
Innov8ProTech
AI maturity
Generic AI features bolted onto legacy systems as an afterthought.
AI-native engineering — models integrated from the first commit, not retrofitted.
Data ownership
Your data flows into vendor clouds and general-purpose training pools.
Deployed on your infrastructure. Data never leaves unless you choose a hosted LLM.
Delivery model
Project-bound engagements that end at handover.
Build, run, and evolve — models monitored, retrained, and improved continuously.
Autonomy
Automation limited to scripted rules and if-then workflows.
Agentic AI with multi-step reasoning, tool use, and human-in-the-loop.
Transparency
Progress shared only at scheduled reporting cycles.
Real-time dashboards — accuracy, latency, cost, drift visible at any time.
Language
English-only models, poorly suited to Ghanaian users.
Twi, Ga, Ewe, Dagbani — plus English and French.
Existing projects
Already have a product?
Add AI without rebuilding.
You do not need to throw away your existing software to benefit from AI. We integrate AI capabilities into what you already have — Laravel, Node, .NET, Java, Python, whatever your stack is.
What most vendors push
Rip out and rebuild
- 18-month rewrite that disrupts every user
- Migration risk — you lose years of business logic
- Team retraining, new bugs, new infrastructure
- AI features still not guaranteed after the rewrite
- Business runs on the old system while the new one is built
How we do it
Augment what you have
- AI features integrated in weeks, not years
- You keep every line of working business logic
- No user disruption — new capabilities ship silently
- Stack-agnostic — Laravel, Node, Python, .NET, Java, Ruby
- Optional handover: clean code your team can extend
Common integration patterns
Chatbot in your CRM
Support agents get an AI copilot that knows your products, customer history, and escalation rules.
Document processing into workflows
Invoices, applications, KYC docs auto-extracted and pushed straight into your back-office.
Recommendations in e-commerce
Your existing store gets personalised product and content suggestions via a single API call.
Fraud detection into payments
Real-time transaction scoring plugged into your payment flow — no changes to processing.
Semantic search across your data
Your existing database gets a vector layer. Users search by meaning, not keywords.
AI email & message triage
Incoming messages classified, prioritised, and routed into your current ticketing system.
Voice-to-text into your forms
Field officers, clinicians, or staff dictate input. Speech converts to structured data.
Predictive alerts into your dashboard
Churn, default, and demand predictions surfaced in the dashboard your team already uses.
Audit
We review your codebase, database schema, and current workflows to find the highest-impact AI opportunities.
Integrate
AI capabilities added via clean APIs and webhooks. Your existing product keeps running, unchanged.
Measure
We track usage, accuracy, cost per call, and business metrics. If it is not working, we tune it or remove it.
AI in our own products
AI, already shipped.
We don't just talk about AI integration. We build it into our own product suite — five platforms, all running production AI today. Here's what's live.
ZuriHIS
Hospital information system
- Clinical triage AI for intake
- Automated ICD-10 medical coding
- Patient no-show prediction
- Drug interaction checker
Designed for
Faster intake, earlier no-show warnings
InnoPharma
Pharmacy management platform
- Per-SKU demand forecasting
- Expiry risk prediction
- Automated reorder suggestions
- Prescription OCR & parsing
Designed for
Smarter stock levels, fewer expiries
SikaNet CBS
Core banking system
- Real-time transaction scoring
- Credit risk assessment
- AML anomaly detection
- Customer service chatbot
Designed for
Real-time fraud detection at scale
Nine9Fleet
Fleet management platform
- Predictive vehicle maintenance
- Route optimisation engine
- Driver behaviour scoring
- Fuel consumption forecasting
Designed for
Lower fuel and maintenance costs
Litigone
Legal case management
- Contract clause extraction
- Case document summarisation
- Legal research assistant
- Precedent matching engine
Designed for
Faster review, better case research
What this means for you
The same AI engineering runs in your product.
Every technique we use in our own products — RAG, fine-tuned LLMs, predictive ML, document intelligence — is available for your engagement. You get battle-tested patterns, not experiments.
Case study
ZuriHIS:
AI inside the hospital.
How we built clinical AI directly into our hospital information system — running on-prem at each site, with patient data never leaving the building.
At a glance
The context
A network of private hospitals and clinics running ZuriHIS as their core patient management system. Multiple sites, thousands of patient encounters every month, and a clinical staff that needed help with intake, coding, and follow-up.
The problem
Manual triage at intake was slow and inconsistent. ICD-10 coding was error-prone, affecting insurance claims. Patient no-shows were a constant drain — clinics blocked slots that never filled.
What we built
An AI-assisted triage module that guides intake staff through structured questions and flags urgency. Automated ICD-10 coding suggestions trained on historical encounter data. A no-show prediction model that identifies at-risk appointments 72 hours in advance, triggering SMS reminders.
How it works
All AI runs inside the hospital's existing ZuriHIS deployment. No external API calls, no patient data leaving the site. The models update nightly from the day's encounters — improving continuously without manual retraining.
What changed
Intake staff move through triage faster with fewer errors. Coding is more consistent, and claim rejections dropped. No-show warnings give the front desk time to fill or rebook slots before they become revenue loss.
What this proves
AI in a regulated, privacy-sensitive sector is possible — if it's deployed the right way. ZuriHIS runs every model on the hospital's own servers. No patient data leaves the building. No external API calls. No exceptions. The same architecture applies to any regulated deployment.
How we work
Discovery, prototype, production, improve.
Most AI projects fail in the discovery phase. We do not skip it.
Discovery
We map the problem, audit your data, and define success metrics. If the data is not there, we tell you before you spend a cedi.
Prototype
Working model in weeks. Real data, real evaluation, real numbers. You see accuracy before you see a contract.
Production
Deployment, guardrails, monitoring, cost optimisation. Models that survive real traffic and real users.
Improve
Continuous evaluation, retraining on fresh data, drift detection. AI is a living system — we keep it that way.
Engagement roadmap
What six months looks like.
Not every project runs this exact timeline, but this is the shape. Milestones, client touchpoints, and decision gates at every phase.
Discovery & framing
Data readiness
Prototype
Production build
Deploy & monitor
How we engage
Three ways to work with us.
Every AI engagement starts with evidence, not a contract. You decide the next step after seeing the last one's output.
Diagnostic
Find out if AI is the right move
Includes
- Data audit — what you have, what's missing
- Problem framing with measurable outcomes
- Feasibility assessment for 2–3 candidate use cases
- Written report with recommended path
- Executive briefing with your stakeholders
Best for
Organisations that aren't sure yet — this replaces a pitch with evidence.
Prototype
Working model before a full commitment
Includes
- Data preparation and pipeline build
- Model training and evaluation on real data
- Accuracy benchmarks documented and honest
- Working demo you can show stakeholders
- Kill/continue decision at the end — no obligation
Best for
Teams that want proof before production. Most clients start here.
Production
Deployed, monitored, and improving
Includes
- Full production deployment on your infrastructure
- Monitoring, alerting, and drift detection
- Guardrails and human-in-the-loop where needed
- Integration with your existing systems
- Handover or ongoing managed operation
Best for
Teams with a proven prototype ready to ship and scale.
Every engagement gets a written scope, timeline, and quote before we start. No surprise invoices.
Risk mitigation
What if it doesn't work?
The honest answer is that it sometimes doesn't. AI is not guaranteed. Here is how we protect you from spending money on something that will not ship.
Kill early if data says so
If the prototype doesn't hit accuracy targets, we tell you and stop. You do not pay for a production phase that won't work.
Exit ramps at every phase
Every engagement has clear decision points. You are never locked into the next phase before seeing the last one's output.
Honest accuracy reporting
We publish real evaluation numbers — including the failure modes. No cherry-picked demos, no vendor fluff.
Fallback when AI fails
Every AI feature has a defined fallback — usually manual process or rule-based logic. Nothing breaks if the model goes down.
Monitoring from day one
Production models are monitored for drift, latency, cost, and accuracy. Problems get caught before users see them.
Data never leaves your control
For regulated sectors, everything runs on your infrastructure. We can build and hand over without ever storing your data ourselves.
Our commitment
If we conclude during a prototype that your data cannot support the use case, we tell you and stop. You keep the prototype and the evaluation report — and pay nothing for a production phase that would not work.
Built for Ghana
AI that speaks
Ghanaian languages.
Speech recognition, text-to-speech, and conversational AI in Twi, Ga, Ewe, and Dagbani — plus English and French.
Twi
Speech · Text · Chat
Ga
Speech · Text · Chat
Ewe
Speech · Text · Chat
Dagbani
Speech · Text · Chat
FAQ
Common
questions.
Let us talk
Ready to put AI
to work?
Tell us the problem. We will tell you honestly if AI is the right tool — and if it is, we will show you a working prototype.
