neesh Inc.

Case Studies

Composite scenarios: fictional companies, written to illustrate what our platform is designed to do. The figures are design targets, not measured client results.

Knowledge base pipeline — Confluence and Drive documents flowing through permission-aware RAG to Slack answers with citations

Your senior staff answer the same 30 questions. Every single week.

CHALLENGE_VECTOR 165 employees. 8 years of institutional knowledge scattered across Confluence, Drive, and SharePoint. Senior partners fielding 30+ interruptions weekly.
SOLUTION_APPROACH Permission-aware RAG connected to all document sources. Answers in Slack in under 10 seconds, every response cited and traceable.
OUTCOME_INDEX Design targets: 73% fewer knowledge interruptions. New hire time-to-productivity: 8 weeks to 3. 14 undocumented processes surfaced by gap detection.
PythonFastAPIPostgreSQL + pgvectorClaude HaikuConfluence APIGoogle Drive APISlack BoltAWS ECS Fargate
  • Your inbox triages itself. The tricky ones still reach a person.
  • New regulation published Tuesday. Your compliance team knew by Wednesday.
  • A complete proposal draft in 3 minutes. Validated before anyone reads it.
  • Weekly competitive intelligence brief in Slack. AI cost: four cents.
  • Invoice data extracted, matched to POs, and queued for approval. Automatically.
InboxPilot architecture — email triage pipeline classifying, judging, and routing tenant emails automatically

Your property management inbox handles 68% of emails. Automatically.

CHALLENGE_VECTOR 240 units. 60+ emails per day. Two property managers spending 90 minutes every morning just triaging — before a single response was written.
SOLUTION_APPROACH FlowBase deployment that classifies every email on arrival, auto-responds to routine requests, queues borderline cases, and escalates urgent or legal triggers.
OUTCOME_INDEX Design targets: 68% of emails handled without human input. Average response time: 3.5 hours to 4 minutes. 11 hours saved per week.
n8nFastAPIPostgreSQLClaude HaikuGmail APIAWS ECS Fargate
  • Drop 50 invoices. Ask what you spent on shipping last quarter.
  • New regulation published Tuesday. Your compliance team knew by Wednesday.
  • SOWs that used to take a day now take a conversation.
  • New hires find answers in 10 seconds instead of asking Sarah.
PriceWatch pipeline — competitor pages flowing through diff engine, ML significance scorer, and LLM narrator to Slack briefing

Your competitor changed their pricing. You found out three days later.

CHALLENGE_VECTOR Four direct competitors. A buying manager with a bookmark folder and a spreadsheet, spending 45 minutes every morning checking sites manually — and missing the moves that happened overnight.
SOLUTION_APPROACH PriceWatch deployed across 8 competitor pages. Local ML filters noise at zero cost. The LLM narrates only the 10% of changes that matter. Weekly briefing in Slack every Monday morning.
OUTCOME_INDEX Design targets: 4 days pre-warning on Black Friday pricing. First competitive response in under 2 hours. AI cost for the week that caught it: $0.04.
PythonFastAPIPostgreSQL + pgvectorClaude HaikuHuggingFace TransformersPlaywrightAWS ECS Fargate
  • Every inbound lead scored and routed before your sales team opens Slack.
  • Monday morning: a briefing on your business metrics for eight cents.
  • Every executive summary in your firm's voice. Not a template's.
  • Support tickets classified and drafted in 4 seconds. Urgent ones skip the queue.
Proposal generation pipeline — wizard context fanning out to parallel specialist agents, converging through a quality gate to a complete document

Your best proposals take 12 hours to write. This one took 3 minutes.

CHALLENGE_VECTOR Every proposal starts from a blank document. Quality varies by deadline pressure, not by what the firm knows. The institutional memory that wins pitches does not accumulate.
SOLUTION_APPROACH 7 specialist agents running in parallel, a review pass for cross-section consistency, and a quality gate that scores every document before delivery.
OUTCOME_INDEX Design targets: complete draft in under 3 minutes. Every document validated at ≥ 7.0 / 10 before delivery. LLM cost under $0.40 per document.
PythonFastAPIPostgreSQL + pgvectorClaude HaikuClaude SonnetClaude OpusNext.jsAWS ECS Fargate
  • New hires find answers in 10 seconds instead of asking Sarah.
  • A competitor posted 3 senior engineering roles. You see the pattern first.
  • Your inbox triages itself. The tricky ones still reach a person.
  • Legal questions answered at 2am. With citations.

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