GenAI Solution Design
Replace fragile demos with dependable assistants and retrieval systems. Reduce hallucinations, control cost/latency, and deliver experiences your stakeholders trust.
The Challenge
POCs don't scale; retrieval quality is uneven; hallucinations and data exposure risks erode trust; costs spike without guardrails.
Our Approach
Use-case-first blueprints for assistants, RAG, and agentic workflows—covering data prep, orchestration, evaluation, and safety. Model-agnostic and enterprise-ready.
Key Outcomes
Ship Real Value
Blueprint to move a priority use case from idea to dependable production path.
Reduce Hallucinations
Retrieval, prompting, and safety tactics that measurably improve answer quality.
Control Cost & Latency
Architecture choices and evaluation to balance quality with spend and speed.
Protect Data
Access controls and minimization patterns designed into the solution.
Accelerate Iteration
Evaluation harness for rapid, repeatable improvements.
Core Deliverables
- • Solution blueprint (diagrams + decision log)
- • Retrieval design (chunking, indexing, grounding data)
- • Orchestration spec (tools, functions, fallback strategies)
- • Evaluation suite definition (quality, safety, latency, cost)
- • Rollout plan with risk controls and rollback/fallback
Cross-links: Operationalize via MLOps & ML Systems; integrate policies with AI Governance & Risk.
Our Method — AtelaMind GenAI Design Loop
Align Outcomes
define value, risks, acceptance criteria
Design Retrieval & Orchestration
data prep + flows
Prototype to Learn
thin-slice validation, fast feedback
Evaluate & Harden
quality/safety gates, regression harness
Plan to Operate
scale path, ownership, controls
Engagement Models
Case Study
European Enterprise — Designed policy RAG with evaluation harness and safe fallbacks, enabling trusted internal answers.
FAQs
Which models?
We're model-agnostic (open/closed weights).
How do you protect data?
Designs apply minimization, masking, and access controls end-to-end.
How are you different from big firms or cloud PS?
Senior specialists, no vendor quota, patterns that work across stacks.
Avoiding lock-in?
Retrieval and orchestration are abstracted so you can change providers.