Artificial intelligence
Enterprise AI consulting, machine learning, AI agents and intelligent automation, from use-case assessment to supported production systems.
Talk to our team- Enterprise sourcesBusiness applicationsData platformDocuments
- Grounded responseRetrievalover governed contentModelEvaluationagainst a measured baseline
- Oversight and actionHuman reviewproportionate to the consequencesSystem of recordthe result reaches the work
- Monitoring, logging and audit trail
Intelligence that reaches production, and stays supported.
Most enterprise AI difficulty is not modelling. It is data readiness, integration with the systems where work actually happens, and the governance needed to run something accountable. Sreehith provides specialist expertise in AI, machine learning, AI agents and intelligent automation. We start from a business process and carry the work through to a supported production system, drawing on our enterprise platform, data engineering and integration experience.
Explore our expertiseHow we can help.
Connected capabilities, shaped around the requirements of your organisation.
Strategy and readiness
Decide what is worth building before building it. Assess candidate use cases against business value, data availability and the cost of being wrong, and identify early those that are not yet viable.
- Business use-case identification and prioritisation
- AI readiness review across data, platform and skills
- Technical feasibility and build-or-buy assessment
- Enterprise AI architecture and platform selection
Implementation
Build the application around the decision it supports. Retrieval-augmented generation grounds a model in your own governed content, which is usually what makes an enterprise answer verifiable.
- AI solution design and development
- Generative AI applications
- Large language model integration
- Retrieval-augmented generation over enterprise content
- Intelligent document processing and extraction
Agents and automation
Automate the workflow steps that suit it, with human review retained wherever judgement or accountability matters, and controls to override or escalate agent actions.
- Enterprise workflow automation
- Agent-assisted business processes
- Intelligent knowledge access and enterprise search
- Human-in-the-loop review and escalation controls
Data and machine learning
Prepare data to a standard the model can rely on, then evaluate against a measurable baseline. Evaluation is an engineering deliverable, not a demonstration.
- Data preparation, labelling and feature engineering
- Machine learning engineering
- Model integration with business applications
- Evaluation, benchmarking and quality testing
- MLOps practices where the platform supports them
Enterprise integration
Connect intelligence to the systems of record so results reach the people doing the work. This is where our ERP, cloud and middleware experience carries over directly.
- ERP and business application integration
- Cloud platform integration
- Data platform integration, including Snowflake
- API design and service integration
- Authentication, authorisation and access control
Governance and responsible use
Make the system accountable. That means knowing what data went in, being able to show why an output occurred, monitoring for drift, and keeping human oversight proportionate to the consequences of a mistake.
- Data privacy and information handling
- Model and prompt evaluation policy
- Responsible AI controls and documented limitations
- Monitoring, logging and auditability
- Proportionate human oversight and review
Maintenance and support
An AI system changes after launch, because its data, its models and its usage all move. Support covers the whole surface rather than the model alone.
- Behaviour and performance monitoring
- Operational troubleshooting and incident support
- Ongoing model and prompt evaluation
- Cost and performance optimisation
- Integration maintenance as source systems change
Typical challenges we address.
If any of these apply to your organisation, our specialists can advise on the right approach.
Discuss your priorities- Pilots that never reach production
- Data not ready for the intended use
- No agreed way to evaluate whether output is good enough
- Unclear accountability for automated decisions
- No support model once a system is live
Relevant technologies and specialisations.
Discuss the support your business needs.
Describe the business process you want to improve, the data and systems involved, and the oversight or assurance your organisation requires.