AI & machine
learning.
End-to-end AI and machine learning engineering, from strategy and applied ML through generative AI, agents, MLOps and responsible-AI governance, delivered by integrated teams.
A complete AI practice from strategy to production.
From applied machine learning through generative AI, agents, MLOps and responsible-AI governance, our practice spans the full lifecycle of production AI delivery.
AI strategy & advisory
AI opportunity mapping, use-case prioritisation, build-vs-buy assessment and value-case modelling, grounded in operational reality rather than trend-driven ambition.
Applied machine learning
Supervised, unsupervised and reinforcement-learning models engineered for production, from feature engineering and model selection through deployment and drift monitoring.
Generative AI systems
Production-grade generative AI applications, retrieval-augmented generation, structured extraction, summarisation and content generation, with prompt engineering, evaluation and cost controls built in from the start.
AI agents & agentic workflows
Autonomous and semi-autonomous agents that reason, plan and act, engineered on tool-use, structured memory and evaluation patterns, with human-in-the-loop controls proportional to risk.
Predictive analytics & forecasting
Demand forecasting, churn prediction, propensity modelling and time-series analysis, engineered for the decision workflows they inform, not delivered as standalone dashboards.
Computer vision
Image classification, object detection, segmentation, OCR and document intelligence, for use cases across manufacturing, healthcare, retail, media and connected devices.
Natural language processing
Text classification, entity extraction, semantic search, summarisation and conversational interfaces, built on modern foundation models and evaluated against domain-specific benchmarks.
Recommendation & personalisation
Recommender systems and personalisation engines, from collaborative filtering through modern embedding and hybrid architectures, with clear serving, cold-start and experimentation strategies.
MLOps & model operations
Model registries, feature stores, deployment automation, monitoring, drift detection, retraining pipelines and rollback pathways, engineered as first-class platform capabilities.
Data engineering for AI
The foundational data plumbing that determines model quality, ingestion, quality, labelling, governance and vector-store operations, designed to support both training and inference at scale.
AI governance & responsible AI
Risk classification, model documentation, bias assessment, safety evaluation and alignment to emerging regulatory frameworks including the EU AI Act, ISO/IEC 42001 and NIST AI RMF.
Evaluation, guardrails & safety
Evaluation harnesses, red-teaming, hallucination and jailbreak testing, and production guardrails, treated as first-class engineering artefacts, not ad-hoc reviews.
A structured, evidence-led approach from prototype to production.
Our delivery framework integrates data science, ML engineering, platform and responsible-AI practice under a single model, designed for transparency, adaptability and outcome alignment.
Discover
A structured discovery to understand the business problem, the data landscape, existing capability and success measures. The output is a shared framing and a phased delivery plan.
Prototype & evaluate
Rapid prototyping and structured evaluation against defined success metrics, validating feasibility, data readiness and value before committing to production build.
Productionise
Iterative delivery of production-grade systems, model, data, serving, observability and human-in-the-loop controls, integrated with client platforms and delivery pipelines.
Operate & improve
Model monitoring, drift detection, retraining, evaluation and governance operations. Knowledge transfer is designed into the engagement, not appended to it.
A modern AI engineering toolchain.
Model, framework and platform selection is guided by your existing landscape, team capability and long-term product strategy, not by preference.
Python
PyTorch
TensorFlow
Hugging Face
LangChainOOpenAILLlamaIndexVvLLM
Pandas
NumPy
Ray
MLflow
Airflow
Databricks
Snowflake
Kafka
Postgres
Elasticsearch
Kubernetes
AWS
Azure
GCPSSageMakerWBWeights & BiasesProgrammes delivered on this practice.
Common questions from prospective clients.
If your question isn't addressed here, please reach us at hello@tekansa.com.
01What types of AI and ML engagements do you deliver?
We deliver a full range of AI engagements, from strategy and opportunity mapping through applied ML, generative AI, agents, computer vision, NLP, recommendation systems and enterprise MLOps programmes. Engagements are scoped around business outcomes and staffed with the right composition of data science, ML engineering, data platform and responsible-AI capability.
02How do you decide between traditional ML, generative AI and agents?
The choice is made per problem based on the shape of the task, data availability, latency and cost profile, and the risk of getting it wrong. Well-defined structured problems often warrant classical ML; open-ended generation and reasoning problems warrant generative AI; workflows that require planning and tool use may warrant agents. We recommend the pattern that best serves the business outcome, not the one that carries the most attention.
03How do you approach evaluation, guardrails and responsible AI?
Evaluation is engineered into delivery from the outset, with domain-specific benchmarks, structured red-teaming, hallucination and safety testing, and production guardrails proportional to risk. Our practice is aligned to emerging regulatory frameworks including the EU AI Act, ISO/IEC 42001 and NIST AI RMF, and we produce documentation and evidence as part of delivery rather than as post-hoc artefacts.
04How do you handle data readiness, quality and governance?
Data readiness typically determines outcome more than model choice. Our practice includes data-landscape assessment, ingestion and pipeline engineering, quality frameworks, labelling operations, feature stores, vector-store operations and end-to-end data governance, designed to serve both training and inference at production scale.
05How do you approach cost, latency and reliability of AI systems in production?
Production AI systems are engineered against defined cost, latency and reliability envelopes, with model selection, caching strategy, batching, fallback pathways and serving infrastructure chosen accordingly. Monitoring covers not only availability and latency but also drift, degradation and cost per outcome, as ongoing engineering disciplines rather than one-off exercises.
Let's discuss your AI engineering needs.
Share a brief overview of your objectives and we'll arrange an introductory conversation with the relevant practice leadership. Every enquiry receives a direct response.