The business challenge
What needs to work better.
Uncontrolled model output can expose data, invent answers or create unpredictable usage bills.
What we deliver
We shape the engagement around a useful first outcome. The scope can include:
- Retrieval and permission design
- Prompt and model evaluation suite
- Cost limits, logging and fallback behavior
How we approach the work
- 01Define the decision and establish a baseline.
- 02Assess data quality, permissions and evaluation criteria.
- 03Build a controlled pilot with measurable tests.
- 04Monitor quality, cost and human escalation.
At each stage, we review the result with your team and record decisions that affect scope, operation or future changes. Explore our delivery methodology.
A decision worth getting right
Use retrieval only over authorized sources and treat retrieved text as untrusted input. Evaluate refusal and uncertainty as well as helpful answers.
Technology with a reason behind it
We select tools against your current systems, quality requirements and operating capacity. Explore the practical considerations for:
Industry context
The delivery plan accounts for the workflows and constraints of your sector. Learn about our approach to:
Questions about generative ai & llm integration
What does generative ai & llm integration include?
A typical scope includes retrieval and permission design, Prompt and model evaluation suite, Cost limits, logging and fallback behavior. The final scope identifies the systems covered, acceptance checks, responsibilities and any exclusions before implementation begins.
What should we decide before starting?
Use retrieval only over authorized sources and treat retrieved text as untrusted input. Evaluate refusal and uncertainty as well as helpful answers.
Which technologies can support this work?
Depending on the requirements, we consider Python, Node.js, PostgreSQL. We compare compatibility, operating cost and the skills needed to maintain the result rather than imposing a fixed stack.
What information helps you estimate the engagement?
For generative ai & llm integration, share your current systems, the affected users, integration documentation and target operating requirements. We use these to define a first phase, identify unknowns and explain what could change the estimate.
