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I'm shaping a modular control layer so property, legal, and advisory teams can treat MCP servers as steady collaborators - observable, auditable, and ready for high-trust work.
The work spans multi-server orchestration, property intelligence, and legal knowledge tooling. I'm codifying how agents request, verify, and escalate actions so specialists can move fast without giving up control.
Tracks in play
MCP control plane · Property intelligence · Legal knowledge ops
Engagement rhythm
Ongoing pilots with property operators, legal reviewers, and my internal R&D sandbox.
North star
Give domain experts faster answers while keeping audit trails, provenance, and override power intact.
The registry governs which tools exist, how prompts are templated, and what each agent is allowed to invoke.
Listings and documents flow through normalization and enrichment so valuation and ops teams get context they can trust.
Data extraction and reasoning layers that help counsel jump straight to obligations, risks, and precedent.
Curated research bundles keep MCP responses fresh, citeable, and aligned to each operator's intent.
Operational signals I watch
Workflow success rate
Watching full workflow executions to keep the happy path tight and flag the outliers that need fixes.
Median response time
4.2s from orchestration to fulfillment, with p99 holding at 19.2s while I trim the edge cases.
Escalation loop
Human-in-the-loop resolution within 15 minutes, logged to the audit trail.
These checkpoints decide what graduates into the shared MCP server library.
Applied experimentation across platforms - architectural choices, iteration pivots, mitigation of failure surfaces. Happy to walk through decision paths & alternatives.
Internal multi-agent orchestration lab exploring coordination primitives, escalation heuristics, capability routing and isolated tool contexts. Result: reduced manual routine workload and reusable secure delegation patterns.
Supervisory agent that scaffolds subordinate agents (tool selection, policy injection, validation loops) and iteratively refines outputs - accelerating prototype cycles and surfacing recursion failure points.
Internal system aggregating pricing cadence, release velocity, hiring deltas & category chatter into weighted directional indicators - informing automation feature prioritization & AI service positioning.
High-volume structured content generation pipeline (domain adaptive prompts + semantic block templating) delivering 100+ weekly pages with consistency, cost compression and engagement uplift vs manual production.
Autonomous outbound qualification workflow - LLM + voice synthesis + intent classification + escalation gating. Filters low-value leads; surfaces enriched qualified prospects with structured context.
Synthetic product & lifestyle image workflow reducing studio cycles & turnaround. Style-controlled prompt system + variant batching enabling faster launch content and iterative marketing experiments.
Earlier web platform work preceded the current AI/automation focus - reinforcing principles: solve concrete problems elegantly, optimize for maintainability, avoid novelty-driven complexity. Built with Django, Wagtail, Tailwind, HTMX.
Automated ingestion & normalization pipeline (OCR, layout extraction, structural tagging) eliminating manual PDF conversion effort - reliable machine-usable downstream content with minimal operator friction.
Data-driven Muay Thai matchmaking platform: skill signal weighting, fairness heuristics & transparent pairing workflow - reducing bias and improving match quality (community free tier).
Multilingual property operations platform: listing ingestion, workflow automation, virtual media support & tri-language interface - reducing friction in cross-border deal flow.
End-to-end relocation workflow: visa pathway guidance, region & cost-of-living intelligence, housing discovery integration, document prep helpers & progress tracking - reducing uncertainty for inbound residents.
Data-enriched property platform combining normalized multi-source listings, valuation signal extraction, geospatial clustering & legal/ownership context surfacing for higher-trust discovery.
We can walk through architecture decisions, iteration tradeoffs, reliability tactics & what we'd refine in a next version.
