PieKBS in Industry

PieKBS is a local-first knowledge search engine built for AI agents. Its architecture — a single binary, SQLite FTS5, plain Markdown, MCP protocol — makes it uniquely suited for industries with strict privacy, compliance, and air-gap requirements.

Why PieKBS Fits High-Privacy Industries

Most RAG systems require external infrastructure: cloud embedding APIs, vector databases, managed SaaS services. PieKBS eliminates every one of these dependencies.

Requirement	How PieKBS Meets It
No cloud data transmission	All data stays local. Zero external API calls for search or indexing.
No embedding model required	Pure SQLite FTS5 + BM25. No vector model, no embedding API, no GPU.
Zero external infrastructure	Single binary + one SQLite file. No Redis, Kafka, Postgres, or vector DB.
Air-gap compatible	stdio MCP mode runs as a local subprocess. Works with zero network access.
Auditable knowledge	All wiki pages are plain Markdown in git. Every change is a diff.
No vendor lock-in	OKF v0.1 compatible. KB is a directory of files — portable to any system.
Local LLM support	Distillation works with Ollama or any local model. Never sends documents to OpenAI.
BYOK (Bring Your Own Key)	If cloud LLM is used for distillation, only the API key is needed — no SaaS subscription.

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Law Firms

Privacy concern: Attorney-client privilege. Client documents cannot leave the firm's infrastructure.

How PieKBS is used:

• Compile case research notes, legal memos, and precedent analysis into structured wiki pages
• Build a firm-wide ADR-style decision library: past deal structures, litigation strategies, settlement patterns
• Each matter gets a source-note; concepts (legal theories, regulatory frameworks) synthesized into concept pages
• piekbs lint validates citation integrity — every claim traceable to a source document
• Git history provides full audit trail for privilege review

Deployment: Single binary on a firm-managed server or laptop. No internet required after initial setup. LLM distillation can use a local model (Ollama + Llama 3) or an on-premise API.

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Accounting & Finance Firms

Privacy concern: Client financial data under NDA, regulatory restrictions on data handling (SOX, GDPR).

How PieKBS is used:

• Structured GAAP/IFRS concept library: each standard becomes a wiki page with related_to links to relevant interpretations
• Internal engagement methodology Wiki — how the firm approaches specific audit types, documented as decision pages
• Compile client-specific knowledge (anonymized) for cross-engagement learning without exposing raw client data
• Knowledge gap analysis (piekbs synthesize --gaps) surfaces under-documented areas before peak season

Deployment: On-premise or private cloud. Distillation with local model keeps client data entirely within firm boundaries.

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Healthcare & Hospitals

Privacy concern: HIPAA (US), GDPR (EU), local health data laws. Patient data cannot touch public cloud.

How PieKBS is used:

• Internal clinical SOP Wiki: nursing protocols, care pathways, post-operative procedures — structured and searchable
• Drug formulary knowledge base: hospital-specific formulary compiled from standard references, kept up to date by the pharmacy team
• Incident and near-miss knowledge base: de-identified case summaries distilled into source-notes, concept pages for root cause patterns
• New resident onboarding: institution-specific procedures and protocols in a searchable Wiki, reducing reliance on senior staff
• Research team knowledge base: literature synthesis, trial design decisions, IRB-approved protocol documentation

Deployment: Air-gapped hospital intranet. piekbs stdio runs as a subprocess of the clinical AI tool. Zero network egress.

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Financial Services & Investment Banking

Privacy concern: Material non-public information (MNPI), trading confidentiality, regulatory requirements (MiFID II, SEC Rule 10b-5).

How PieKBS is used:

• Internal compliance process Wiki: structured documentation of approved trading practices, escalation procedures, pre-clearance workflows
• Deal knowledge base: for each completed transaction, a source-note captures the deal structure, key decisions, and rationale — builds institutional memory across deal teams
• Regulatory interpretation library: compliance team maintains a Wiki of how the firm interprets specific regulatory rules, with contradicts links flagging conflicting interpretations
• Risk model documentation: quantitative analysts document model assumptions, limitations, and validation findings as decision pages

Deployment: Air-gapped trading floor infrastructure or private cloud with no public internet path. Distillation uses on-premise LLM.

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Manufacturing & Industrial

Privacy concern: Trade secrets, proprietary manufacturing processes, IP in equipment design.

How PieKBS is used:

• Plant SOP Wiki: standard operating procedures for each production line, searchable by process step, equipment type, or product family
• Equipment troubleshooting knowledge base: distilled from maintenance logs, service bulletins, and technician field notes — agents query it during repairs
• Quality defect pattern library: historical defect data compiled into source-notes; concept pages synthesize recurring root causes
• New employee onboarding: structured knowledge base reduces dependency on experienced workers for process knowledge transfer
• Supplier and materials knowledge: spec sheets, substitution history, and vendor reliability notes compiled as wiki pages

Deployment: Factory floor OT network, often physically isolated from the internet. PieKBS runs on a local server; technicians query via MCP through their AI coding assistant or custom chat interface.

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Government & Defense

Privacy concern: National security, classified information, data sovereignty requirements.

How PieKBS is used:

• Policy interpretation Wiki: structured documentation of how specific regulations are applied in practice — searchable by policy area
• Citizen service knowledge base: FAQ and procedure guides for government service delivery, maintained by subject-matter experts
• Cross-agency knowledge sharing: common operational procedures compiled into a shared Wiki, reducing duplication across departments
• Procurement and compliance knowledge: past procurement decisions documented as decision pages, with rationale and alternative options considered

Deployment: Sovereign cloud or on-premise with no external connectivity. piekbs serve behind an internal network boundary. MCP over stdio for embedded agent use. OKF-compatible KB format enables knowledge transfer across secure systems.

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Energy & Utilities (Nuclear, Oil & Gas)

Privacy concern: Critical infrastructure protection, proprietary operational data, physical air-gap requirements.

How PieKBS is used:

• Operations and maintenance knowledge base: equipment manuals, incident reports, and procedure updates distilled into structured wiki pages
• Safety and regulatory compliance Wiki: plant-specific interpretations of NRC/HSE regulations, emergency procedures, safety case documentation
• Knowledge retention for aging workforce: experienced engineers' tacit knowledge captured as source-notes and concept pages before retirement
• Incident learning library: post-incident reports distilled and cross-linked — related_to edges surface similar past events during future incident response

Deployment: Physically air-gapped operational technology (OT) network. PieKBS runs with zero external dependencies. Distillation batch-processed offline; index serves agent queries in real time.

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Research Institutions & Think Tanks

Privacy concern: Pre-publication research confidentiality, grant compliance, institutional IP.

How PieKBS is used:

• Literature synthesis: researchers drop papers into raw/, PieKBS distills source-notes and synthesizes concept and comparison pages across the corpus
• Research decision records: methodology choices, hypothesis revisions, data source evaluations documented as decision pages
• Cross-project knowledge sharing: common methodologies and findings compiled into a shared institutional Wiki
• Knowledge gap analysis: piekbs synthesize --gaps identifies under-explored areas before grant proposal writing

PieKBS's specific advantage here: This is the closest to PieKBS's original design intent. Agents search the knowledge base iteratively, follow related links across papers, and synthesize their own conclusions — exactly how a researcher works.

Deployment: Researcher's own machine or institutional server. No cloud required. Local Ollama model for distillation keeps pre-publication data entirely offline.

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Private Deployment Architecture

For all industries above, a typical PieKBS deployment looks like:

[Local KB directory]
  raw/           ← drop source documents here
  wiki/          ← distilled pages (Markdown + git)
  index/         ← SQLite FTS index (auto-managed)

[PieKBS binary]
  piekbs serve ← HTTP MCP + Web UI + file watcher
  piekbs stdio ← subprocess MCP for embedded agents

[Agent integration]
  Claude Code / Cursor / any MCP client
  → connects via http://localhost:8766/mcp (HTTP)
  → or spawns piekbs stdio (no network)

[LLM for distillation]
  Ollama (local)  ← fully offline
  or on-premise API endpoint

No data leaves the boundary. No external services required. The entire stack runs on a single machine or internal server.

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Comparison with Cloud RAG in Regulated Industries

Factor	Cloud RAG	PieKBS (local)
Data leaves premises	Yes (embedding API, LLM API)	No
Vendor lock-in	High (Pinecone, OpenAI, etc.)	None (plain files, SQLite)
Compliance audit	Hard (black-box vectors)	Easy (git diff, Markdown)
Air-gap capable	No	Yes
Infrastructure required	Vector DB, embedding service, LLM API	Single binary
Knowledge portability	Low	High (OKF-compatible directory)
Setup time	Days to weeks	Minutes
Cost at query time	Per-API-call	Zero