- contact@verticalserve.com
Frontier LLMs are great at the engineering desk. The plant floor surfaces four constraints a fine-tuned domain SLM is built to meet.
Plants, mines, ships and field operations frequently have intermittent or no connectivity. A small quantized SLM that runs on a handheld or plant-local server keeps technicians productive when the cloud is unreachable.
Generalist models translate your manuals inconsistently. A fine-tune that knows your equipment, parts and failure language across languages keeps every shift on the same page.
OEM part numbers, failure modes and your plant's tribal terminology are nowhere in a generalist's training data. A fine-tune learns your equipment vocabulary — the difference between "sounds plausible" and "actually correct."
Summarizing every work order, scoring every safety incident, or assisting on every maintenance call at $5–$30 per 1K calls breaks ops budgets. A 7B SLM does the same workload at a fraction of the cost.
From manuals, maintenance logs and SOPs to a deployed manufacturing SLM — runnable at the edge.
Equipment manuals, SOPs, work orders, maintenance logs, safety bulletins, technician notes
Layout-aware parsing, OCR, image / diagram extraction, dedup, equipment-vocab alignment
Q&A pairs from manuals, summarization pairs from work orders, RCA reasoning traces
Qwen / Llama / Mistral base, SFT + LoRA / QLoRA, multilingual mixture across plant languages
Plant task suite, technician-rated benchmarks, safety probes, multilingual quality
Quantized GGUF / AWQ on edge devices, vLLM for plant servers, drift alerts, OTA updates
Same dataset hash → recipe → model → scorecard lineage as the rest of InsightLM. Plant teams get reliable retrains; reliability and EHS teams get version-pinned production behavior.
Each card shows the task, input, output and a target quality / cost bar.
Answer "how do I reset the compressor controller?" or "what's the torque spec for these bolts?" — grounded in the right manual section, in the technician's preferred language, on a handheld device.
Compress a work order's history (notes, parts used, prior interventions) into a structured summary for the next shift, the planner, and the reliability engineer.
Map free-text technician notes to your FMEA / FRACAS taxonomy — per equipment class, per plant, per region — with confidence per code so reliability has a clean signal.
Given a recurring issue, retrieve similar past cases, summarize the patterns, and propose candidate root causes — each grounded in a cited prior work order or manual section.
Turn a technician's voice or text incident description into a structured EHS report — with severity, classification, immediate actions and follow-up — ready for the safety lead to review.
Same model, same prompts, same eval suite — in English, Spanish, Portuguese, Mandarin, German, Polish, Vietnamese and more. Local technicians get answers in their language; corporate reliability gets aggregated signals.
The bar an InsightLM manufacturing SLM is designed and evaluated to. Customer-specific scorecards are produced from held-out evaluation sets during a pilot.
| Manufacturing Task | Metric | Generalist Frontier LLM (Bedrock Claude / Copilot GPT, zero-shot) | InsightLM Fine-Tuned 7B (edge) |
|---|---|---|---|
| Manual / SOP QA | Answer correctness | ~78% | ≥ 92% (target) |
| Work-order summarization | Planner rating (1–5) | ~3.6 | ≥ 4.3 (target) |
| Failure-mode classification | Top-1 accuracy | ~78% | ≥ 92% (target) |
| RCA usefulness | Reliability-engineer rating | ~3.5 | ≥ 4.2 (target) |
| Safety report quality | Safety-lead rating | ~3.7 | ≥ 4.3 (target) |
| Median latency at edge | p50 / p95 | n/a (cloud-only) | ~200ms / ~800ms (target) |
| Cost per 1K calls (edge SLM) | USD | ~$5–$30 | ~$0.005–$0.10 (target) |
Bedrock Claude / Copilot GPT for engineers; existing CMMS (Maximo, SAP PM) for work-order management; InsightLM for the technician-facing, edge-runnable, multilingual layer.
Technician assist, work-order summarization, failure-mode classification, RCA, safety reports. Tasks where edge runtime, multilingual consistency and per-call cost are decisive.
Technicians on handhelds use the SLM; reliability engineers and OEM partnership teams at corporate use Bedrock Claude or Copilot GPT for complex multi-document research.
R&D research, OEM contract review, capital-project memos. Frontier LLMs are the right tool here — an SLM would be over-engineering. InsightLM does not try to win these.
Pick the pattern that matches your connectivity reality, OT network posture and shift cycle.
Quantized GGUF / AWQ models on technician handhelds, ATEX-rated tablets or rugged laptops. Fully offline-capable; OTA updates push new model versions when devices come back online.
vLLM / SGLang on a single GPU server in the plant DMZ; serves all handhelds and HMIs in the plant. Survives WAN outages, no PHI / IP leaving the plant.
For reliability-team aggregation, RCA across plants, and corporate reporting: vLLM on managed GPU instances in your AWS / Azure / GCP VPC. Same model, same prompts as the edge.
Edge SLM handles the technician layer; corporate uses Bedrock Claude for engineering research. One model registry, one observability dashboard.
InsightLM curation pipelines turn each source into model-ready training data — with equipment-vocabulary alignment and lineage tracked end-to-end.
OEM equipment manuals, service bulletins, plant SOPs, lockout-tagout procedures, P&IDs, troubleshooting trees.
CMMS work orders, maintenance logs, inspection reports, technician notes, parts usage, downtime records.
Safety bulletins, incident reports, near-miss logs, quality non-conformance records, audit findings, FMEAs.
A typical pilot picks one or two of the use cases above, runs end-to-end on a sample of your manuals and CMMS data inside your environment, and produces a technician-rated scorecard against your current Bedrock / Copilot baseline in 4–8 weeks.
Edge-runnable • Multilingual out of the box • Complements Bedrock and Copilot