Select, adapt, evaluate, and ship language models that fit your latency, cost, and compliance envelope.
Gensten helps enterprises build LLM capability the right way: model selection, prompt and tool schemas, supervised fine-tuning / preference optimization where needed, evaluation gates, secure hosting (Azure AI Foundry or private), and continuous improvement inside ADLC. We focus on measurable task performance - not model hype.
Most ROI comes from retrieval quality, tool design, and evaluation - not training from scratch. We start with strong foundation models, add RAG and tools, then fine-tune only when data and KPIs justify it.
Content filters, PII redaction, regional data residency, and audit logs are part of the build - especially for BFSI and healthcare agents.
Rarely from scratch. Most enterprises succeed with hosted frontier models + RAG + tools, then targeted fine-tuning. We help you decide with a cost/quality decision matrix.
Selecting models, designing prompts and tool schemas, evaluating quality/cost/latency, optionally fine-tuning, and hosting securely - all inside an ADLC release process.
When RAG and prompting plateau on style, domain jargon, structured outputs, or specialized classification. We require enough curated examples and clear eval gains before training.
Parameter-efficient methods that adapt a base model with smaller trainable adapters - faster and cheaper than full fine-tuning while preserving most base capabilities.
Often yes for high-volume, narrow tasks. We compare frontier models vs. SLMs on your eval set for cost, latency, and accuracy, and may distill after a strong teacher model is proven.
Task accuracy, groundedness (with RAG), toxicity/safety, latency percentiles, and cost per successful task - tracked in Test & Release gates and again in production Monitor.
Yes. Options include Azure AI Foundry/Azure OpenAI with network isolation, or self-hosted stacks (vLLM/Triton) in your VPC for stricter data residency needs.
We define strict JSON/tool schemas, validate arguments, apply policy checks, and log every call so agents can act in enterprise systems without unconstrained free-form actions.
Routing (cheap model first), caching, shorter contexts via better RAG, SLM distillation, rate limits, and budget alerts tied to OpenTelemetry cost attributes.
Prompting is one layer. We deliver the full stack: retrieval, tools, evals, hosting, and ADLC operations so prompts stay effective as data and workflows change.
The new tech-stack SDLC for AI agents - Plan, Code & Build, Test & Release, Deploy, Operate, and Monitor with experimentation and runtime optimization loops.
Production Retrieval-Augmented Generation: chunking, embeddings, vector search, reranking, citations, and agent-ready knowledge retrieval.
Build, evaluate, and operate enterprise agents and models on Microsoft Azure AI Foundry - with governed catalogs, tools, and Foundry IQ intelligence patterns.
Design, build, and operate enterprise AI agents that plan, use tools, orchestrate workflows, and deliver measurable business outcomes.
Talk to Gensten about ADLC, RAG, LLM building, Foundry IQ, Fabric IQ, and OneLake - scoped to your KPIs and compliance needs.