Nelfet.
LLM products
Generative AI
Production-grade LLM applications engineered for measurable quality, observable behavior, and grounded outputs.
95%+
Target quality score on tuned evaluation sets
<1s
Typical first-token latency target
any model
Provider-agnostic architecture
overview
We start by framing the task precisely: what correct looks like, what failure modes matter, and what a labeled eval set must cover.
From there: structured prompts, schema-enforced outputs, retrieval where grounding is needed, and per-feature cost and latency instrumentation.
Quality targets are defined against your labeled gold cases. Model and config selection is gated on your eval set — accuracy, latency, and cost balanced.
what we build
01
Copilots embedded in your product with coherent multi-turn context
02
Schema-enforced structured extraction and summarization
03
Eval harness integrated into delivery pipeline
04
Per-feature cost and latency observability
05
Confidence-aware fallback for out-of-scope queries
how it works
From input to outcome.
- 01
Frame
Define task, failure modes, and labeled eval set precisely.
- 02
Build
Structured prompts, typed outputs, retrieval, fallback paths.
- 03
Evaluate
Every change scored against eval set before merge.
- 04
Observe
Cost, latency, quality signals emitted per request in production.
use cases
Common applications.
01
In-product copilots
Contextual assistant retrieves from your systems, generates targeted drafts.
02
Content operations at scale
Draft, rewrite, summarize — with human review at the right threshold.
03
Structured extraction from unstructured documents
Contracts, forms, filings → typed data structures downstream systems consume.
04
Intelligent classification and routing
Semantic intent tagging routes high-value items with explainable rationale.
05
Multilingual content adaptation
Adapts tone, register, and terminology — not just literal translation.
IndustriesHealthcareRetail & commerceFinance