BRAMKAS / Specialist services
Generative AI Integration
Evaluate a practical AI use case, connect the relevant data and tools, and test the resulting workflow against clear acceptance criteria.
The opportunity
Expertise shaped around your requirements.
Begin with the task and the people who will use the result. BRAMKAS helps assess data readiness and choose an approach, which may involve an existing model, retrieval, tool integration, or a tailored development effort.
Build a limited pilot before expanding. Agree evaluation criteria, access controls, human review, and an operating approach. Model quality, cost, and usefulness depend on the use case and should be measured with representative inputs.
When to choose this service
- You have a specific workflow or knowledge-access problem to evaluate.
- You can provide an accountable owner, suitable data, and human review for a pilot.
What you receive
- Use-case scoping and feasibility assessment
- Data and source readiness review
- Model and retrieval approach selection
- Application and tool integration
- Evaluation and human-review design
- Monitoring and operating considerations
How we help
From the requirement to the work.
Use-case scoping and feasibility assessment
Data and source readiness review
Model and retrieval approach selection
Application and tool integration
Evaluation and human-review design
Monitoring and operating considerations
The benefits
What this makes possible.
The practical advantages of bringing the right expertise into your organization.
Test usefulness before committing to a wider rollout
Identify data and integration constraints early
Compare approaches against a defined task
Include evaluation and human oversight in the workflow
Understand operating and usage considerations
Expand from a pilot when the evidence supports it
How the service connects
Connect business context to an AI application.
Explore the relationship between source information, model development, and application integration.
Illustrative service model
Select a stage to explore
Evaluation and user feedback guide further refinement.
Inside this stage
Context & data
Define the problem and assess the available information, data quality, and requirements before choosing an approach.
What takes shape
- Use-case definition
- Data and source assessment
Inside this stage
AI workflow
Develop and evaluate the model or retrieval workflow, then connect it to the application and its requirements.
What takes shape
- Model or retrieval workflow
- Evaluation and integration approach
Inside this stage
Application
Bring the AI capability into a usable workflow and use evaluation and operational feedback to guide improvements.
What takes shape
- Application integration
- Monitoring and evaluation feedback
A conceptual view of the service. The scope and deliverables are agreed for each engagement.
Try a process simulationAdjust four factors and explore how effort shifts between stages.
Illustrative planning model. Effort units show relative emphasis within this model; they are not hours, prices, success probabilities, or a BRAMKAS delivery estimate.
Where effort shifts
19 effort units- Context & data6 units
- AI workflow8 units
- Application5 units
This matches the reference scenario of 19 units. Most attention in this model: AI workflow.
Playback visualizes relative effort, not elapsed delivery time.
What to clarify first
- Bound the use case and define a small evaluation set.
- Test the highest-risk integration in a limited pilot.
- Assess data access, quality, and representative examples first.
- Identify security, domain-review, and approval owners.
How this model works
All factors use a level from 0 to 2. Readiness is inverted: ready = 0, partly ready = 1, early discovery = 2. All other factors increase from the first option to the last.
Input stage = 2 + scope + (2 × readiness) + dependencies.
Specialist work = 3 + (2 × scope) + (2 × complexity) + readiness.
Output stage = 2 + complexity + (2 × dependencies).
The reference uses level 1 for every factor. These teaching weights are authored assumptions, not measured BRAMKAS performance. Each bar uses the same 0–13 scale. Playback takes 450 ms + 90 ms per effort unit for each stage; it is shortened to a static result when motion is disabled.
Our process
A clear path from brief to delivery.
Know what happens next, from understanding the requirement to supporting the engagement.
Discovery & Strategy
We work with you to identify high-impact use cases for generative AI and develop a strategic roadmap.
Data Assessment
We evaluate your data assets and requirements, ensuring you have the right data to train effective AI models.
Model Development
We develop and fine-tune AI models tailored to your specific use cases and business requirements.
Integration & Deployment
We integrate AI solutions into your existing systems and workflows, ensuring seamless adoption.
Monitoring & Optimization
We provide ongoing monitoring, evaluation, and optimization to ensure your AI solutions deliver maximum value.
The next step
Let’s move your next priority forward.
Tell us what you need. We’ll help you identify a practical next step.