Grounded generation
Define the task and acceptable result for Grounded generation before choosing models, prompts or supporting data.
AI & Intelligent Systems
Generative AI
Gromnii designs production generative AI applications grounded in business context.
Requirement
Context
Reason
Tools
Control
Outcome
When this is useful
Use generative AI when work depends on drafting, summarizing, transforming or reasoning over language, images or structured context at a scale that fixed templates cannot handle well. Production use needs grounding, output controls, evaluation and a clear boundary between generated content and authoritative records.
What Gromnii builds
Structured outputs
Use representative source data for Structured outputs, including difficult cases that expose uncertainty or bias.
Multimodal experiences
Measure Multimodal experiences against task-specific quality, latency and cost limits rather than one generic score.
Enterprise integration
Connect generative AI to approved enterprise systems through narrow interfaces that expose only the data and actions required for the intended task.
Evaluation & controls
Set confidence and impact rules for Evaluation and controls, and send uncertain cases to a person with the evidence needed to decide.
How the AI system is controlled
This reference shows one possible Generative AI arrangement. The actual design depends on the systems, constraints and controls involved.
01Business context
02Grounded context
03Model reasoning
04Approved tools
05Human control
06Evaluation & operation
What matters in production
Data boundaries
Limit prompt and context data to what the use case needs, and separate public, internal and sensitive sources so the model does not mix information across trust levels.
Output quality
Evaluate the generated output for factual support, task completion, instruction following and unacceptable content using examples that reflect real user requests.
Human review
Place review where generated content can affect customers, records, legal obligations or other material outcomes, while allowing low-risk drafting tasks to remain efficient.
Cost and latency
Measure response time and model cost by task, then choose model size, context length, caching and routing according to the quality the workflow actually needs.
What it can improve
Faster knowledge work
Reduce repetitive drafting, summarization and transformation tasks while keeping source context and review where accuracy matters.
More useful natural-language interfaces
Let users work with enterprise information through controlled language interactions without exposing unrestricted system access.
More consistent generated output
Use structured outputs, grounding and evaluations so generated content follows required formats and quality thresholds.
Additional technical detail
Technical implementation notes for Generative AI.
Show additional technical detail
Turn context into useful generation
Prompts, context, retrieval, tools, models, and structured outputs work as one controlled path.
Knowledge work acceleration
Reduce time spent searching, drafting, summarizing, and synthesizing enterprise information.
Operational interfaces
Give users natural-language access to systems and processes with appropriate controls.
Customer and employee experiences
Assistive experiences grounded in approved content and business rules.
Generative AI capabilities
The implementation is shaped by who will use it, what information it may access, how outputs are checked, and where those outputs go next.
01Custom generative AI applications
Create task-specific applications that combine models with business data, workflows, controls, and structured outputs.
02Enterprise AI assistants and copilots
Give employees or customers contextual assistance inside approved workflows rather than a disconnected general-purpose chat window.
03Generative search and content intelligence
Retrieve relevant sources, synthesize information, and preserve grounding so generated answers can be checked.
04Multimodal AI applications
Combine text, image, audio, or document inputs where the task depends on more than one information format.
05Structured generation and function calling
Constrain model outputs to usable schemas and approved functions so downstream systems receive predictable inputs.
06Natural-language business interfaces
Let users query or operate business systems in ordinary language while preserving permissions and system rules.
Related solutions and industries
Discuss a Project
Describe what Generative AI should change, the systems it must work with and the constraints that matter.