AI Development Services

AI project budgets are usually spent before success is defined, let alone measured. We first construct a business case, and then a roadmap that can survive the procurement process. This ensures that every dollar spent serves a specific purpose and is followed by a clear path to a result.

• Assess AI readiness and maturity
• Establish responsible AI frameworks (ethics, bias, governance)
• Design AI operating models and CoEs
• Perform AI cost-benefit analysis and build business cases
• Consult on model risk and compliance
• Evaluate AI technologies and select unbiased platforms

It is faster to buy AI capability than build it, but only if any ownership of the integration and the maintenance of the IT systems and the costs post-implementation is assumed. We assume that ownership.

• Integrations for native LLMs (GPT, Llama, Claude, Gemini)
• Vision as a service: OCR, quality inspection, object detection
• IVR, speech-to-text, and voicebots
• Custom AI APIs
• Predictive analytics APIs for fraud and churn
• Document intelligence APIs for extraction and classification

All too often, the infrastructure under AI initiatives is left unbuilt, and that is where we come in. We build that layer with orchestration, vector stores and scalable compute infrastructure that your team can extend after we have left.

• LLM application frameworks & integration (LangChain, LlamaIndex)
• Multi-model orchestration platforms
• API gateways, middleware & service integration
• Vector database integrations (Pinecone, Weaviate, FAISS)
• GPU infrastructure & compute optimization
• Infrastructure for prompt and context management

In the real world, prompts that are not specific to your situation quickly break down when encountered with the messy input of real users. Between your team and live hallucinations are a fine-tuned model, a RAG pipeline, and cautionary controls that are specific to your situation and Pareto's 80/20 law.

• Custom LLM fine-tuning and training
• RAG systems
• GenAI assistants for legal, marketing, coding, and HR
• Enterprise AI copilots
• Autonomous AI agents and workflows with enterprise guardrails
• Conversational assistants based on ChatGPT

We only create personalization features if there is sufficient behavioral data to justify the personalization, as that is the only situation in which personalization is profitable. We create core features that are personally relevant to the user, and measure our impact in terms of concrete increases in conversion as opposed to simply checking a feature off the list.

• AI-driven UX personalization
• Business core planning
• Behavioral insights and prediction of heat maps
• Adaptive UI based on sentiment
• A/B testing with ML
• Conversion funnel analysis and prediction of drop off

We almost never find off-the-shelf models that solve our clients’ business problems, so we build and train custom ML models from scratch. Before we deploy any models, we compare them to the simplest approach, benchmarking them to ensure we have the highest business impact. More complex models will be deployed to our clients’ live production environments only later.

• Recommendation Systems
• Natural Language Processing and Understanding
• Computer Vision Models: Classification and Segmentation
• Time-Series Forecasting Models
• Deep Learning and OCR Pipelines

The pipelines that we build determine the level of trust that we can have in the forecasts that we generate. We first build the analytics foundation, and then build models that your team will be able to maintain on their own. We will not need to work with your team in order for them to maintain the models.

• Predictive and Prescriptive Analytics
• Forecasting Models: Demand, Revenue, Churn
• Exploratory Data Analysis and Interactive Visualizations
• Customer Segmentation and LTV Modelling
• Big Data Processing and Analytics Pipelines
• Statistical Modeling and Hypothesis Testing

Automation increases the speed of a process, even if that process is broken and causes you to lose money. We always audit a workflow before we automate anything. We will only automate a process if that is determined to be worthwhile.

• AI-powered RPA: document intelligence plus workflow
• Robotic process discovery with machine learning
• Smart bots for procurement, HR and finance
• Automated testing with AI test generators
• Anomaly detection in DevOps & infrastructure logs
• Intelligent document processing for contracts and invoices

A model that is perfectly positioned at launch can start failing silently within a few months, typically long before anyone actually notices a drop in performance. We keep our models' accuracy from drifting with real-world data with versioning, drift detection, and continuous monitoring.

• Model lifecycle management (MLflow, Kubeflow)
• Continuous training / CI-CD for AI pipelines
• AIOps for incident prediction & infrastructure optimisation
• AI model versioning, drift detection & monitoring
• Feature store implementation & management (Feast, Tecton)
• AI code remediation: turning generated code into production software