Generative AI & RAG
Scoped assistants and evidence-linked document workflows. Representative evaluation and access rules are part of discovery.
AI APPLICATIONS / FULL-STACK DELIVERY
A business task.
A system that supports it.
AI applications for knowledge, documents and tool-connected work, with scope, evaluation and ownership defined from the start.
AI SOLUTIONS / A DEFINED BUSINESS TASK
Make knowledge accessible.
Make work move.
Choose the workflow first. We shape the interface, data, AI and integrations around the task.
01 / SCOPED ENGAGEMENT
Knowledge assistants & RAG
Help a team find and use approved policies, manuals and business knowledge through a purpose-built search and answer workflow.
Explore deliverables 02 / SCOPED ENGAGEMENTDocument processing workflows
Turn repeated document reading and data entry into a structured extraction, validation and exception-review workflow.
Explore deliverables 03 / SCOPED ENGAGEMENTAI agents & tool integration
Connect an assistant to approved tools so it can retrieve context, prepare actions and move a defined business task forward.
Explore deliverables 04 / SCOPED ENGAGEMENTAI evaluation & improvement
Assess an AI prototype or a defined application workflow before investing in more prompts, models or automation.
Explore deliverablesGenAI · RAG · Machine learning · APIs & tool integration
See capability scope ↗TECHNOLOGY WITH A DELIVERY SCOPE
The right capability.
For the actual task.
These are discovery and delivery areas, not a list of completed client deployments.
Machine learning
Classification and decision-support models grounded in the founder’s evaluated ML work. Forecasting and other tasks need their own data and feasibility assessment.
MCP & agentic integration
Assessed per tool and workflow. Authorization, approval and failure recovery come before additional autonomy; dedicated MCP delivery evidence is still to be developed.
Multimodal workflows
Images, scanned documents and audio can be assessed in a targeted pilot. Supported formats, privacy, extraction quality and operating cost must be tested.
Fine-tuning & model adaptation
A conditional next step after a baseline and data-rights review. No completed enterprise LLM fine-tuning engagement or custom foundation-model training is claimed.
Evaluation & operations
Task quality, latency, usage cost, regression checks and a defined review process. Support and monitoring commitments follow the project agreement.
HOW TO ENGAGE DREAMVIEW
A defined first step.
A path beyond launch.
Start with discovery, commission a focused build, or agree on ongoing care. Deliverables and responsibilities are written down.
Decide what to build.
Workflow map, data and integration assessment, acceptance plan and scoped proposal.
Deliver one valuable workflow.
Working software, agreed evaluation, deployment documentation and owner handover.
Keep the system useful.
An agreed maintenance scope, release checks, usage review and prioritised improvements.
LET’S MAKE IT PRACTICAL
A useful next step.
Bring the problem. Let’s work out the right scope and approach.