Capabilities overview

Production AI platforms for enterprises, institutions, and founders.

From agentic orchestration at enterprise scale to governed data platforms for civic institutions — systems that turn fragmented operations into a single, governed, human-supervised layer across enterprises, institutions, and founders.

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Engagement networkOne operating standard

Enterprise AI
District Connect
LIFT
Mentis
Meridian

Enterprise AI & Silicon MLOps

Enterprise AI & MLOps Modernization

Two production engagements delivered for a leading AI computing company, Azure-native

Seven siloed RAG agents — each on its own stack — consolidated into a single agentic platform. An MCP router and Agent-to-Agent protocol give users one conversational surface across Teams, Slack and Web, with every backend preserved behind lightweight adapters — zero rewrites, zero regression risk. A parallel OpsML engagement modernized semiconductor test automation.

Unified experience

One bot across Teams, Slack & Web via the M365 Agents SDK.

Intelligent orchestration

Dual-path MCP routing, A2A cross-agent handoffs, secure document orchestration.

Enterprise foundation

Centralized Entra ID auth, unified Redis + Azure SQL state, one telemetry view.

Phased delivery

Discovery → Foundation → A2A → Scale → Optimize.

View full engagement detail

Civic & Cultural Institutions

District Connect

For a major cultural & academic district — unifying a leading art museum, a research university & a premier music center

A privacy-first relationship-intelligence layer that unifies engagement history, event calendars, and donor insight across anchor institutions — without replacing any of their existing CRMs.

Activation

Community concierge, cross-district discovery portal, cooperative campaign studio.

Intelligence

360° supporter mapping, next-best-action engine, shared affinity scoring.

Foundation

Explicit consent tracking, staff-review mandate — never autonomous outreach.

Architecture

Sits above each institution's existing CRM; salted-hash identity resolution.

View full project document

Workforce Intelligence

LIFT — Labor Intelligence for Tomorrow

For a regional economic development partnership — a regional workforce-intelligence platform

A governed, shared view of regional labor demand, supply, and program outcomes — three analytic views plus an AI Navigator grounded entirely in that same governed data, so partner staff get cited answers instead of assembling reports by hand.

Regional Pulse

KPI summary, demand-intensity map, demand-vs-training trend.

Occupation Drill-Down

Demand/supply, skills, geography, ranked program relevance.

Program Funnel

Six-stage conversion, bottleneck analysis, intervention workspace.

View full engagement brief

Hiring & Talent

Mentis — AI Interviews. Human Decisions.

Series A conversational AI interview platform, live & pilot-ready

Voice and video AI interviewers hold adaptive, GPT-4-driven conversations with candidates 24/7. A scoring engine returns evidence-backed assessments and an integrity layer flags anomalies for human review — Mentis assesses and recommends, but humans always decide.

1 · Create

Upload a job description; AI generates a structured interview blueprint in 30 seconds.

2 · Interview

Candidates interview on their own time; the AI adapts follow-ups in real time.

3 · Decide

Recruiters review an evidence-backed scorecard and make the final call.

View full project brief

Education

Meridian — School Management Platform

Piloting at St. Augustine's Senior Secondary School (887 students, est. 1929)

One modular, open-source platform (AGPL-v3) unifying alumni engagement, school administration, and AI-enhanced learning — three systems most schools run separately, or not at all — behind a single login and shared data model for staff, students, parents and alumni.

Profiles & directory

Self-service alumni profiles, searchable by class year and batch.

Transparent giving

Integrated donation processing, quarterly reporting, milestone-based releases.

Events & mentorship

RSVP & ticketing, job board, mentor matching, engagement analytics.

View full project brief

Have a challenge similar to one of these stories?

A discovery call can quickly map the right solution pattern, timeline, risks, and next steps.

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Technical papers

AI capability papers, open to everyone.

These papers walk through the architecture, delivery framework, and governance controls behind our AI capability work in full technical depth.

AI Solution Delivery

13 min read

Engineering AI to a Verifiable Definition of Done

Most enterprise AI pilots never reach production. This paper sets out a governed, phase-gated delivery framework — Discover, Design, Build, Validate, Scale — for taking AI systems from proof of concept to a verifiable Definition of Done, with human review at every gate.

Why AI pilots stall before production, and the failure patterns behind it
A phase-gated delivery framework with named exit criteria at every stage
Illustrative scenario: consolidating clinical-documentation AI across a multi-site healthcare network

Read the full paper

AI Lead Intelligence

12 min read

Closing the Loop: From Visitor to CRM Record

Customer and lead data loses its value the moment it sits unprocessed. This paper describes a vendor-agnostic, AI-assisted data pipeline that turns a raw form submission into an enriched, deduplicated CRM record within seconds — with every enrichment step treated as best-effort, never as a gate that can lose a record.

Why manual data handling loses signal between first contact and a usable CRM record
A signed, event-driven architecture connecting forms, enrichment, and CRM
Illustrative scenario: a B2B professional-services firm closing its customer-data gap

Read the full paper

AI Answer-Engine Growth

14 min read

A Governed AI System for Search Visibility

Most organizations already have the data that shows exactly where their content is falling short — search-console reports, competitor rankings, and analytics — but turning that into a shipped page change is a manual, low-priority chore. This paper outlines a governed AI system we design for clients: one that drafts, reviews, and ships content and structured-data changes from search and competitor signals, through the same pull-request workflow engineering teams already trust.

Why manual content audits produce recommendations that never ship
A closed-loop architecture spanning search data, AI drafting, and human-reviewed publishing
Illustrative scenario: a specialty B2B technology brand closing a visibility gap against larger competitors

Read the full paper

FAQ

Frequently asked

What kind of work does Banjubits take on?

Production AI platforms and systems across enterprises, institutions, and founders — from agentic orchestration at enterprise scale to governed data platforms — all built to a single, human-supervised operating standard.

Are these engagements real client projects?

They are anonymized capability overviews drawn from real production work. They show the architecture, delivery approach, and outcomes without exposing client or partner identities.

Can I read the technical detail behind an engagement?

Yes. The technical papers on this page walk through the architecture, delivery framework, and governance controls behind our AI capability work in full technical depth, and are open to everyone.

I have a similar challenge — how do I start?

Book a discovery call. It quickly maps the right solution pattern, timeline, risks, and next steps for your specific use case.