Knowledge Base Systems
Turn your docs into a system your team can actually use.
- Internal search across policies & SOPs
- Structured knowledge, not just files
- Decision trees for consistent answers
AI-Powered Systems
Build systems that think, answer & act
Knowledge bases, AI assistants, and automated workflows — built to replace scattered decisions with reliable systems.
Knowledge Base Systems
v1.2Structured internal knowledge, SOPs, decision trees
Live modulekb_01
AI Assistants
v1.0Employee-facing assistants powered by internal knowledge
Live moduleai_02
Workflow Automation
v1.4n8n workflows, forms, approvals, notifications
Live modulewf_03
Operational Decision Systems
v1.0Rules, routing, checks, and repeatable actions
Live moduleods_04
01 · Solutions
What I build
Four solutions that turn manual operations into systems your team can rely on — not generic AI tools.
AI Assistants (Powered by your data)
Internal AI bots that answer real questions.
- Answer instantly using your knowledge base
- Always grounded in internal data
- Reduce dependency on senior team members
Automations & Workflows
Systems that connect tools and execute decisions.
- Automate tasks across tools (Google Sheets, Slack, APIs)
- Apply rules and validation automatically
- Return structured outputs
Example: A new request lands in Google Sheets → the system validates it, applies your rules, and returns a decision — no one has to touch it.
Learn moreDashboardsSoon
Visual command centers for your operations.
- Real-time KPIs from your existing tools
- Custom views per role or department
- One place for decisions, not scattered tabs
These are internal systems built around how your operations actually work — not generic AI tools.
02 · How it works
How systems actually get built
A repeatable method that turns messy operations into systems your team can rely on.
- 01
No visibility → mapped workflows you can trust
We map what's actually happening before touching any tools.
→ Support tickets, HR requests, internal workflows
- 02
Scattered docs → one system your team can rely on
Docs, threads, and tribal knowledge in one searchable place.
→ Knowledge bases, decision trees, retrieval layers
- 03
Decisions depend on people → rules the system follows
How decisions get made becomes logic the system runs.
→ Rules powering AI agents and automated responses
- 04
Hours of repeated work → automated leverage
Only what saves hours, not minutes, gets automated.
→ APIs, n8n workflows, integrations
- 05
Different answers every time → one consistent outcome
Same input, same output. No variation.
→ Consistent answers across teams, users, and time
03 · Systems
This is how your operations start running themselves
This is a real system we shipped — our most recent build — not a demo. It's how internal decisions start running automatically.
Internal data + decision rules + AI → automated decisions
HR · Automation
Automated salary alignment checks
Internal decision-support system for HR teams
88%
of salary inquiries resolved without HR involvement
Instant
answers delivered in seconds, not days
Consistent
decisions across employees in the same role
Problem
- Salary questions required manual data lookup
- No consistent comparison logic across employees
- Decisions depended on individual HR judgment
- Hard to scale with increasing request volume
Solution
A workflow pulls employee and peer compensation data, applies salary-band rules, and returns a structured decision automatically.
- Retrieves employee and peer compensation data
- Applies predefined salary bands and role levels
- Compares against internal benchmarks
- Returns a structured, consistent decision
HR Assistant·Salary Alignment
Live system
Request
"Is my salary within the expected range for my role and team? (Senior Backend Engineer)"
System steps
- 1Retrieved peer group (same role, level, and department)
- 2Checked current salary against band range
- 3Applied internal compensation rules
- 4Generated structured decision
Decision
Within rangeSalary is within the expected range for your role. Positioned at P55 of the band — no adjustment needed.
What used to depend on individual judgment now runs on consistent, repeatable logic.
Stack
n8nOpenAISupabaseInternal KBWorkflows
04 · Results
What changes after the system is live
Real outcomes from internal decision-support systems already in production.
88%
Hands-off requests
The team stops handling repetitive work.
Days → Seconds
Answers, not waiting
Employees get answers instantly.
Same decision.
Every time.
No variation
One outcome, no matter who's on shift.
05 · About
Who I am
I turn messy operations into systems that run on clear decisions.
Five years in customer support showed me where operations break: repeated questions, unclear processes, and decisions left to whoever's on shift.
I don't just build automation. I design how decisions should be made.
Stack
Tools I reach for when designing operational AI systems.
n8nOpenAI / ClaudeQdrantSupabasePostgresREST APIsWebhooksVector DBs
06 · Contact
Tell me what you're trying to fix.
Describe what's breaking. I'll show you exactly how a system would handle it.
- Internal knowledge structure your team will actually use
- AI assistants connected to your real documents and processes
- n8n automations that reduce manual decisions
Replies within hours · Mon–Fri