Generative AI Assistants and Agents Built for Production Work
Kramora engineers generative AI systems that hold up after the demo. That includes assistants grounded in your own documents through retrieval, agents that call approved tools to complete multi-step tasks, and the evaluation, guardrails and human review that keep both accountable. Each system is designed around a specific workflow, the permissions of the people using it and the business systems it needs to read from or act on.
What We Deliver
LLM integration
Model selection, prompt design, structured outputs and routing across hosted and self-managed models through OpenAI-compatible APIs, with cost and latency tracked per request.
- Model comparison on your own tasks
- Structured outputs validated against schemas
- Fallback routing between models
Retrieval-augmented generation (RAG)
Ingestion, chunking, embedding and hybrid search over your content, with permission filtering at query time and citations back to the source passage.
- Policy and procedure assistants with citations
- Search across tickets, wikis and shared drives
- Access control inherited from source systems
Multi-step agents
Agents that plan and execute a sequence of steps toward a goal, with explicit state, step limits and checkpoints where a person can intervene.
- Case research and summary preparation
- Data gathering across several systems
- Triage that drafts, routes and follows up
Tool calling
Typed, permission-scoped tools that let a model query systems, create records or trigger workflows, with every call logged and side effects controlled.
- Read-only lookups in CRM and ERP
- Draft-then-approve record creation
- Idempotent actions with audit logs
Evaluation
Test sets built from real tasks, automated scoring for grounding and correctness, and regression checks that run before every prompt or model change.
- Golden question sets with expected answers
- Retrieval relevance and faithfulness scoring
- Side-by-side comparisons before release
Guardrails
Input and output controls that handle prompt injection, sensitive data, off-topic requests and policy limits before a response reaches a user or a system.
- Personal data detection and redaction
- Injection screening on retrieved content
- Topic and action allowlists
Human-in-the-loop
Review queues, approval steps and confidence thresholds that decide when the system acts alone and when a person signs off.
- Approval before external messages are sent
- Reviewer feedback captured as evaluation data
- Escalation when confidence is low
Business Problems & Meaningful Outcomes
Solution Architecture
Illustrative reference architecture for a governed assistant that retrieves enterprise knowledge, calls approved tools and routes sensitive actions to human review.
Assistants are delivered through chat, existing portals, collaboration tools or APIs. Tool definitions are versioned contracts with typed inputs, so model changes do not break integrations.
Retrieval respects the permissions of the person asking, and data sent to any model provider is reviewed for sensitivity and residency needs. Prompts, responses and tool calls are logged under agreed retention rules.
Agents start with read-only access and gain write actions one at a time, each behind validation and approval. Every action is attributable to a user, an agent run and a specific tool call.
How We Work
Pick a workflow where generative AI can help, collect real example tasks and agree what a good answer or completed action looks like.
- Prioritized use cases
- Seed evaluation set from real tasks
- Risk and data-sensitivity review
Design retrieval, model choice, tool boundaries, guardrails and review points around the workflow and its risk level.
- RAG and agent design
- Tool and permission map
- Guardrail and review policy
Build iteratively against the evaluation set, testing grounding, tool behavior and adversarial inputs before any user sees it.
- Working assistant or agent
- Evaluation and red-team results
- Pilot release plan
Release to a pilot group, monitor quality and cost, and widen scope as evidence accumulates.
- Quality and cost monitoring
- Reviewer feedback loop
- Expansion roadmap
Technology Choices
Example Scenarios
Policy assistant for an insurance operations team
A regional insurer's operations staff search long policy manuals and underwriting guidelines to answer routine broker questions, and answers vary depending on who responds.
Kramora would build a retrieval-augmented assistant over the approved document set, filtered by each user's access and returning cited passages. An evaluation set drawn from real broker questions would gate every release, and answers on poorly covered topics would be flagged for a specialist.
Agent-assisted service request handling
An internal IT team receives access and equipment requests by email, and staff manually check entitlements in several systems before creating tickets.
Kramora would design an agent that reads each request, looks up entitlements through read-only tools and drafts a ticket with its reasoning. A coordinator approves or edits each draft, and those decisions feed the evaluation set before any step becomes automatic.
Frequently Asked Questions
RAG fits when answers depend on documents that change, need citations or must respect access permissions. Fine-tuning helps when you need a consistent format, tone or narrow task behavior that prompting cannot reach. Many production systems use retrieval for knowledge and light tuning, or none, for behavior.
Related Solutions
Adjacent Services
Industries We Serve
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