Engineering Solutions That Ship

Manual procurement processes cause stockouts and excessive inventory costs in energy operations.

AI-driven supply chain that monitors inventory, ALWAYS checks ERPNext preferred suppliers first for stock and pricing, falls back to web-scraping alternatives only when preferred can't fulfil the order, then auto-generates purchase orders for human approval.

Field technicians lack real-time access to equipment manuals, safety procedures, and diagnostic data at remote sites — and the live RAG / sensor stack is the slow path even when the answer is one a previous tech already worked through.

Edge-deployed AI agent that ALWAYS checks the local Qdrant cache of recent diagnostics + common procedures first. If a high-confidence cached answer exists it's returned immediately; only on a cache miss does it fall back to live ChromaDB RAG + sensor fusion. Voice interface, offline-capable, hands-free.

Product requirements get lost in translation between specification documents and development tasks.

AI pipeline that parses PRD documents, generates structured task breakdowns, and syncs bidirectionally with project management tools.

Organizations need AI-powered knowledge search but cannot send sensitive documents to external cloud services.

Self-hosted RAG system with enterprise document management, end-to-end encryption, and complete data sovereignty.

Energy sector companies lose leads through manual follow-up processes — contact form submissions go unanswered, calculator users leave without engagement, and the same contact gets re-scored as a new lead each time they fill out a different form, fragmenting CRM history.

Multi-touchpoint lead capture system that ALWAYS checks ERPNext CRM for an existing matching contact (by email + company + phone) before scoring a new lead. Duplicates are merged and routed to the existing AE; only genuinely new contacts trigger the 3-email welcome drip + scoring + lifecycle migration.

Context evaporates faster than it can be typed. Names, follow-ups, and half-formed ideas are lost between the moment they happen and the next time someone sits at a desk. Traditional CRM and notes apps assume a structured capture session that rarely happens in real life.

A 10-second Telegram voice note becomes a transcript, an entity-extracted JSON blob, a Neo4j graph node, a Qdrant vector, and — if a follow-up is implied — a Frappe CRM Lead and a parked Listmonk draft. A Claude-powered research agent autonomously prospects via web search, staging leads for human approval. Every outbound message stops at a human approval gate before sending. A nurture sequencer sends RAG-grounded email sequences based on CRM stage.

Battery capacity discharge tests and cell impedance measurements are safety-critical, standards-intensive procedures. Field engineers juggle IEEE 450/1188/1106 standards, manufacturer-specific thresholds, and temperature corrections while managing arc flash and hydrogen hazards — with paper checklists and manual calculations. Engineers also lose context between work orders — observations, anomalies, and follow-ups don't reach the maintenance system until paperwork is filed days later.

AI-guided field testing workflow that auto-selects the correct IEEE procedure by battery type, performs real-time impedance trending against baseline, monitors discharge cell-by-cell for reversal or rapid decline, calculates temperature-corrected capacity, and generates an IEEE-compliant report before the engineer leaves site. A purpose-built Telegram bot accepts voice or text notes from any field engineer and routes them through the same pipeline — no laptop required.