RAG and knowledge systems
Make internal content, product knowledge, and domain documents findable, attributable, permission-aware, and useful.
✓Generative AI development
Generative AI development services for grounded products.
The model is only one layer. We connect it to the right content, retrieval strategy, product experience, guardrails, and evaluation loop so the system earns trust over time.
RAGgrounded and attributable answers
Evalsrepresentative quality testing
Multi-modelquality, latency, and cost choices
What we deliver
Built around the use case, not the buzzword.
Sketric develops RAG, document intelligence, LLM applications, and generative AI products with retrieval, evaluation, guardrails, and cost controls.
Document intelligence
Extract, classify, compare, summarize, and route information from the documents that slow teams down.
✓LLM product experiences
Design interfaces that expose sources and uncertainty and help people move from an answer to the next useful action.
✓Model and cost orchestration
Use the right model for each task while keeping latency, privacy, fallbacks, and unit economics visible.
✓Where this creates leverage
A strong fit when the difficult part is the whole system.
We scope delivery around the operating reality, the people responsible for it, and the evidence needed to make the next decision.
People cannot find answers across scattered knowledge
A governed retrieval layer can connect private or changing content to grounded answers with visible source material.
Documents create a recurring operational bottleneck
Extraction and generation can become a reviewable workflow instead of an isolated summarization prompt.
A model feature needs measurable product quality
We define representative inputs, expected evidence, refusal behavior, human review, latency, and cost before scaling usage.
Evidence in production
Proof from products and client systems.
Owned applications and selected client work show how AI decisions behave after launch.
Questions teams ask
Useful answers before the first call.
Direct answers for teams deciding whether an AI system is worth building.
When should a company use RAG?+
RAG is useful when an AI system needs to answer from changing, private, or domain-specific material instead of relying only on general model knowledge.
Can you work with our existing model provider?+
Yes. We can design the system around the provider, models, deployment choices, and data controls that fit the product and its requirements.
How do you measure generative AI quality?+
We define representative questions, expected evidence, answer quality, refusal behavior, latency, cost, and human review thresholds before scaling usage.
Can a RAG system respect document permissions?+
Yes. Access controls should carry into retrieval so a user or agent can only discover and cite material they are permitted to see.
Do you build the interface around the LLM?+
Yes. Source visibility, review, edits, follow-up actions, feedback, and failure handling are part of the product experience.
Have a project in mind?
Bring us the workflow, constraint, or ambitious idea.
We will help identify the narrowest useful starting point and the evidence needed to move forward.
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