Connect
Approved documents, data, streams and applications enter through defined interfaces and identities.
Technology & Services
How we build and deliver enterprise AI.
A shared technology foundation supports focused engineering services—from data and models to applications, automation, integration and dependable operation.
Technology foundation
Five control points from approved input to monitored outcome.
The implementation changes by workflow, but identity, data boundaries, model choice, evidence, human approval and observable operation remain central.
Prepare
Information is validated, transformed, indexed and separated according to access boundaries.
Reason
Models, retrieval, analytics, rules and tools are selected for the actual task.
Review
Sources, confidence, exceptions and approvals remain visible to accountable users.
Observe
Quality, failures, latency, usage and cost are measured against agreed expectations.
Engineering services
Specialist capabilities for building dependable AI systems.
Large Language Model Adaptation
Adapt language models to domain tasks with controlled data, measurable acceptance criteria and release gates.
- Supervised fine-tuning using reviewed examples
- Efficient model-adaptation methods
- Preference-data preparation
- Domain dataset curation
- Training and regression evaluation
Model Evaluation & Safety
Measure task quality, grounding and failure modes before release and throughout operation.
- Task-specific evaluation suites
- Unsupported-answer and source-grounding checks
- Adversarial and safety scenarios
- Human and model-assisted review
- Quality, response-time and cost regression
Labelling & Human Feedback
Create dependable training and evaluation data through clear guidelines and layered quality assurance.
- Text, image and video annotation
- Response ranking and preference data
- Expert review workflows
- Guideline and gold-set design
- Inter-annotator quality measurement
Artificial Intelligence Harness & Agent Engineering
Engineer the context, tools and controls that make model behaviour useful inside a real workflow.
- Prompt and context architecture
- Retrieval-augmented generation for grounded answers
- Tool use and agent coordination
- Guardrails and human approval
- Tracing, feedback and observability
Delivery approach
The decisions made before production.
Scope, controls and operating ownership are designed around the application—not added after the build.
Protect the working context
- Approved sources and accountable owners
- Identity and role boundaries
- Retention and deletion requirements
- Logging and sensitive-field handling
Measure the useful outcome
- Task-appropriate model selection
- Retrieval and citation requirements
- Evaluation data and acceptance measures
- Fallbacks for low-confidence outputs
Keep people accountable
- Human approval points
- Exception and escalation paths
- Application programming interface and application integration
- Traceable inputs, outputs and actions
Design for the real environment
- Cloud, private-cloud, on-premise or edge
- Availability and latency expectations
- Quality, drift, failure and cost monitoring
- Ownership for support and change
Selected technology ecosystem
The right technology for every layer of the solution.
The final stack follows the client's environment, constraints and support model. These categories show the technologies we can assemble around the solution.
Artificial Intelligence & Machine Learning
- Python
- PyTorch
- TensorFlow
- OpenAI
- LangChain
- OpenCV
- Hugging Face
Data & Analytics
- PostgreSQL
- SQL Server
- Snowflake
- Databricks
- Apache Spark
- Power BI
- Tableau
Adaptation & Training
- Supervised fine-tuning (SFT)
- Low-rank adaptation (LoRA)
- Parameter-efficient fine-tuning (PEFT)
- Transformer reinforcement learning (TRL)
- DeepSpeed
- Weights & Biases
- MLflow
- Versioned datasets
Knowledge Retrieval & Agent Harnesses
- Meaning-based retrieval
- Knowledge graphs
- Retrieval-augmented generation (RAG)
- Tool calling
- Agent coordination
- Guardrails
- Human approvals
Evaluation & Safety
- Reviewed reference datasets
- Grounded-answer evaluation
- Adversarial testing
- Model-assisted evaluation
- Human evaluation
- Safety testing
- Regression gates
Serving & Observability
- Model gateways
- Inference serving
- Prompt tracing
- Quality monitoring
- Cost and latency
- Feedback loops
- Cloud, private or edge
The delivery stack is confirmed during solution design.
