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A complete pipeline from raw enterprise data to a deployed, monitored vertical SLM
Ingest documents, Q&A, glossaries, transcripts and tickets. Parse, deduplicate, scrub PII/PHI, classify, and version every dataset with full lineage.
Generate domain Q&A, instructions, reasoning traces and hard negatives from your corpora using teacher-LLM distillation and human-in-the-loop labeling.
Fine-tune with reusable recipes — SFT, LoRA/QLoRA, DPO/ORPO, continued pretraining. Score every candidate against domain eval suites and red-team probes.
Quantize (GGUF / AWQ / GPTQ), serve via vLLM / SGLang / llama.cpp, gate with guardrail SLMs, and monitor drift, cost and quality from a unified registry.
InsightDLM connectors turn your existing knowledge into model-ready training sets
Policies, contracts, manuals, SOPs, glossaries, regulatory filings — parsed with layout-aware extraction and OCR fallback.
Call transcripts, chat logs, agent notes, support tickets and emails — turned into intent, summarization and dialog training pairs.
CRM, ERP, claims, transactions and product catalogs — converted into extraction, classification and reasoning training data.
Three integrated planes — Curation, Training and Operations — designed to be reused across every vertical you build for.
A complete set of building blocks — no notebooks duct-taped together
Qwen, Llama, Mistral, Phi, Gemma — pinned, signed, ready to fine-tune
YAML-defined SFT / LoRA / DPO recipes, versioned alongside your data
Q&A, instructions, reasoning traces, adversarial cases from your corpora
Held-out test sets, LLM-as-judge with rubrics, regression gating per release
Lineage from raw source → dataset hash → recipe → model artifact → scorecard
Domain-tuned embeddings and grounded answer generation out of the box
Small classifier SLMs for PII redaction, safety, refusals and topic gating
vLLM / SGLang / llama.cpp — deploy in your VPC, your edge, or private cloud
One architecture. Three model tiers. Routed automatically by confidence, SLA, cost and data sensitivity.
There is no single right model for an enterprise. InsightDLM stands up a portfolio of Domain Language Models — both Small (SLM) and Large (LLM) — and serves them alongside frontier LLMs (Claude, OpenAI) via Bedrock or Azure secure inference. An intelligent router picks the right model per request: SLMs absorb high-volume bounded work at the lowest cost and tightest SLA, domain LLMs handle complex reasoning on your data, and frontier LLMs cover the broad reasoning DLMs aren't designed for.
0.5B–14B params · Qwen / Llama / Mistral / Phi / Gemma · served in your VPC
20B–70B params · quantized for cost-effective serving · in your VPC or on-prem
Secure inference inside your cloud account · no model training on your data
SLM-tier DLMs excel at bounded, well-defined work. They are not the right tool for open-ended novel reasoning, multi-step chain-of-thought across unfamiliar domains, queries requiring the latest world knowledge, or broad creative drafting. For those, the router escalates to a Tier 2 domain LLM or to Tier 3 (Claude / OpenAI via Bedrock or Azure).
When frontier reasoning is needed, InsightDLM routes to Claude or OpenAI through Bedrock or Azure OpenAI secure inference — never direct hosted APIs. Inference stays inside your cloud account boundary, under your IAM / VPC controls, with customer-managed keys, enterprise DPAs / BAAs, and your audit trail. No model training on your data.
Submission, underwriting, claims, customer 360 and compliance — with a side-by-side comparison vs. frontier LLMs.
Concrete examples of domain-specific small language models you can build — and the tasks they solve
Qwen fine-tuned on policy wordings, claims notes, ACORD forms and call transcripts — for underwriting, claims and customer service.
Fine-tuned on product catalogs, reviews, support tickets and merchandising guidelines — for catalog quality, search and customer experience.
Tuned on KYC docs, statements, disclosures, transaction logs and contact-center transcripts — for risk, compliance and customer operations.
Trained on clinical notes, payer policies, drug labels and literature — deployed entirely on-prem to meet HIPAA / PHI requirements.
Fine-tuned on contracts, case law, regulatory filings and internal playbooks — for contract review, due diligence and policy QA.
Trained on equipment manuals, maintenance logs, SOPs and safety bulletins — runnable at the edge inside plants and field operations.
Tuned on rate plans, network knowledge bases and millions of support interactions — for self-service, agent assist and churn prevention.
Fine-tuned on statutes, forms, benefits handbooks and curricula — fully on-prem for sovereignty and data-residency requirements.
Don't see your vertical? InsightDLM is designed to be re-targeted — bring your domain corpora and we'll help you stand up the first model.
Talk to Us About Your DomainTrain and serve entirely inside your environment. No data, no gradients, no model weights ever leave your network.
Deploy InsightDLM in your own data center, VPC (AWS / Azure / GCP), or air-gapped environment. Bring your own GPUs or use managed clusters.
Built-in PII / PHI detection and redaction during curation. Per-dataset access controls, encryption at rest and in flight, full audit trails.
Designed to support GDPR, HIPAA, SOC 2, PCI-DSS and CCPA programs with dataset lineage, license tracking and reproducible training runs.
Stop renting a generalist LLM API. Own a small, fast, accurate model trained on your data — built with InsightDLM.
On-prem deployment • Your data never leaves your network • Enterprise support included