Artificial Intelligence
Disciplined AI strategy and delivery — ML, computer vision, and NLP applied to business decisions like demand forecasting, document extraction, and support triage. We start from the decision you need and work backward to the model, with a human-in-the-loop where the cost of error is real.
Overview
AI is a means to a business outcome, not a milestone to check off a transformation roadmap. AI Solutions is the disciplined application of machine learning, computer vision, natural language processing, and generative AI to solve a specific operational problem — forecast demand, triage support tickets, inspect product defects, detect fraud, optimise pricing — where the model earns its keep in measurable business value. We start from the decision the business needs and work backwards to the model, framing every use case around the cost of error, the available training data, and the integration workflow. Where a simple rules engine or statistical model solves the problem cheaper, we recommend it. Where the problem genuinely requires AI, we design, build, integrate, and govern it — with a human-in-the-loop wherever a wrong prediction carries real cost. Built on foundation models accessed through our GPU infrastructure (NVIDIA A100, H100, H200, B200) and managed through our AI Studio platform.
Clevertek scopes every engagement to your environment — capacity, sites, compliance and support model — so you get a tailored plan rather than a fixed SKU. Pricing is quote-only, and our solutions architects will work through your requirements before any proposal.
Our approach
We design, build, and operate production AI solutions for specific enterprise use cases. Every engagement starts with use-case validation: we identify the business decision, assess whether AI is the right approach (versus a rule or a simpler model), evaluate available data quality and volume, and define the success metrics and cost-of-error boundaries. We then build the model using foundation model APIs, fine-tuning on customer-domain data, or custom training depending on the problem complexity. Models are wired into the business workflow through REST APIs, event-driven pipelines, or embedded inference, with a human review step for high-cost-of-error scenarios. Every deployed solution includes monitoring for data drift, model performance degradation, inference cost, and fallback-path health. We manage the full lifecycle — from problem framing through deployment and ongoing model governance — so the AI investment produces measurable, sustained business value.
Why work with us
Value-first AI, not technology-first
We start with the business decision and work backwards to the model. Where a rule or a simpler regression solves the problem cheaper, we recommend it. AI is deployed only where it measurably outperforms alternatives.
End-to-end GPU-backed infrastructure
Every solution runs on our GPU infrastructure — NVIDIA A100, H100, H200, and B200 — with optimised inference runtimes (TensorRT-LLM, vLLM) for low-latency serving. No separate infrastructure procurement or GPU environment setup.
Governance built into every deployment
Data drift monitoring, model performance tracking, inference cost per prediction, fallback paths on model failure, and human-in-the-loop for high-cost errors. Governance is not bolted on after deployment.
Integrated with AI Studio platform
Solutions integrate with the AI Studio platform for experiment tracking, model registry, and inference serving. A model validated in a PoC graduates to production through the same toolchain without reimplementation.
Key benefits
What this solution delivers for your business.
Measurable business outcomes from AI investment
Every solution is framed around a specific business decision with defined success metrics, cost-of-error thresholds, and regular performance reviews. AI spend is tied to outcomes, not activity.
Faster path from idea to production
Foundation model APIs and managed GPU infrastructure eliminate the months-long cycle of infrastructure procurement, environment setup, and model training from scratch. A validated PoC can reach production in weeks, not quarters.
Controlled cost and risk per use case
Inference cost per prediction is tracked from day one. Human-in-the-loop guardrails prevent costly wrong predictions from reaching automated business processes. Cost ceilings and fallback paths are configured before deployment.
Avoid AI projects that fail in production
Data drift monitoring, model performance decay detection, and automated retraining triggers prevent the common failure pattern where a model works at launch and degrades silently over time as data distributions shift.
What's included
Part of this managed service.
Use-case validation and scoping
Structured assessment of the business decision, available data, cost-of-error boundaries, and AI feasibility. We produce a build-or-buy recommendation with estimated ROI, risk assessment, and implementation timeline.
- Business decision framing with stakeholders
- Data availability and quality assessment
- Cost-of-error and risk boundary definition
- Build-versus-buy recommendation with ROI estimate
Model development and fine-tuning
Solution-specific model development using foundation model APIs, supervised fine-tuning on customer-domain data, or custom PyTorch/TensorFlow training. Experiment tracking with MLflow, hyperparameter tuning with Katib.
- Foundation model API integration
- Supervised fine-tuning on domain data
- Custom PyTorch/TensorFlow training
- MLflow experiment tracking and provenance
Production integration and serving
Models deployed as REST APIs, event-driven inference pipelines, or embedded inference on GPU infrastructure. Optimised serving with TensorRT-LLM and vLLM for low-latency inference.
- REST API and event-driven deployment
- TensorRT-LLM optimised inference
- Human-in-the-loop review workflows
- Fallback paths on model failure
Model governance and monitoring
Continuous monitoring of data drift, model performance metrics, inference latency, prediction distribution, and cost per prediction. Automated retraining triggers and alert thresholds for model degradation.
- Data drift and concept drift detection
- Performance metric tracking per model
- Inference cost and latency dashboards
- Automated retraining triggers
Where it helps
Real-world scenarios where this solution delivers measurable outcomes.
Customer support ticket triage
A large e-commerce company receives 50,000 support tickets daily across email, chat, and social media. An NLP model classifies each ticket by category (billing, returns, technical, account), assigns priority, and routes to the correct team. The solution reduces first-response time by 60% and routes 40% of tickets to self-service resolution paths, with escalation to human agents for complex or high-sentiment cases.
Predictive demand forecasting for retail
A retail chain with 500 stores and 20,000 SKUs deploys a demand forecasting model that predicts weekly sales for each product-location combination. The model ingests historical sales data, promotional calendar, weather data, and local event feeds. Forecast accuracy improves from 65% to 85%, reducing stockouts by 30% and excess inventory write-offs by 25%.
Questions buyers actually ask
Do we need a dedicated data science team?
For common use cases (classification, forecasting, document extraction), our managed AI solutions handle the model and integration — no dedicated data science team required. A data science team is justified only for genuinely novel problems where the model architecture or training approach itself is the innovation.
How do you control AI cost and risk?
Cost per prediction is tracked from day one with configurable ceilings and budget alerts. Human-in-the-loop guardrails prevent costly wrong predictions from reaching automated processes. Fallback paths ensure business continuity if the model degrades or the API is unavailable.
What happens when the model is wrong?
We design for that with a fallback path (rule-based logic, default behaviour, or human escalation) and a review step. The cost of error is assessed during use-case validation, and guardrails are configured proportional to that cost — low-cost errors run unattended, high-cost errors escalate to a human reviewer.
Can you work with models we already have?
Yes. We can take existing models and deploy them on our GPU infrastructure with monitoring and governance, or build new solutions that complement the models you already run. The governance and integration layer works regardless of the model origin.
Ready to scope a solution?
Talk to a Clevertek solutions architect about your requirements — no obligation.