Custom built AI tooling and applications that transforms manual workflows into growth, productivity and revenue engines

The Client's Original Problem

Client recruiters were manually reformatting every candidate resume they received — copying content from a candidate's own .docx or PDF into the Client's branded Word template, matching section structure, fixing formatting, and aligning dates. This repetitive, time-consuming work was done on every single submission, with no consistency guarantee and significant room for transcription errors.

The Bespoke AI Solution

Resume Refinery is a full pipeline with three distinct AI stages:

1. Azure Document Intelligence extracts raw text and structure from any .docx or .pdf (including scanned documents via OCR), computing word-level confidence scores to flag low-quality inputs.
2. Two-pass LLM extraction (GPT-5 models): Pass 1 faithfully extracts all content verbatim (zero inference — typos preserved). Pass 2 synthesizes practice-area summary, key highlights, and confidence.
3. Template rendering + validation loop: Populates the Client-branded Word template with perfect formatting, runs an LLM-as-Judge, and includes an auto-fix loop. Delivered as web app, Teams bot, and CLI on Azure with Entra ID SSO.

The Client's Original Problem

Analysts at a law firm were manually processing commercial insurance policy PDFs — documents that can be 300 pages containing 40+ individual forms in a single file. The work involved visually scanning pages, manually transcribing red-text values, and starting from scratch on every new policy. A single policy took 50+ hours with 10–20% error rates.

The Bespoke AI Solution

PolicyBurst automated the entire pipeline end-to-end:

1. Azure Document Intelligence (OCR with full layout awareness)
2. Detected form boundaries using footer patterns and Schedule of Forms index
3. PyMuPDF color analysis to extract red-text values
4. Reconstructed exact document structure in Word format
5. Produced reusable templates + policy matrix Excel
6. Processed up to 15 PDFs concurrently on Azure

The Client's Original Problem

External contractors were submitting invoices via DOCX files and email. Internal staff had to re-key data, chase exact rate validation against master tables, and route approvals through inboxes—with no single audit trail and heavy friction getting clean, consistent files to accounting.

The Bespoke AI Solution

Invoice Hub is a full billing pipeline on Azure (React, Hono, PostgreSQL, Terraform) with four integrated layers:

1. Self-service portal — Calendar-based time tracking, matter-filtered rates, draft auto-save, JWT auth with optional Azure Entra ID SSO.
2. Validation & workflow — Exact rate match against imported master data; single-approver Approve/Reject links via email; immutable audit log of submissions and decisions.
3. Accounting handoff — On approval, generates a fixed-format Excel and queues email to the accounting inbox via Microsoft Graph (async with retry).
4. Azure OpenAI — "Enhance Description" on line items so service text is clearer and more professional for billing review.

The Client's Original Problem

Organizations needed a structured, repeatable way to understand AI readiness across people, data, process, technology, governance, and talent—without commissioning a bespoke consulting study every time—and to turn answers into a narrative report with benchmarks, not a static checklist no one acts on.

The Bespoke AI Solution

AI Maturity Assessment pairs a production questionnaire with Azure-hosted AI reporting:

1. Branching questionnaire — 23–40 questions with conditional logic across six dimensions (adoption, data, process, technology, governance, talent).
2. Azure AI Foundry — GPT-family model scores vs industry benchmarks, gap analysis, prioritized recommendations, phased plan; validation, retries, and PDF export.
3. Magic-link access — Azure Communication Services email; no passwords; users can revisit past reports anytime.
4. Microsoft Bookings — "Schedule consultation" from the report; Terraform + Container Apps + Front Door for production hosting.

The Client's Original Problem

Consulting teams were juggling spreadsheets and email to track dozens to hundreds of AI use cases per client—slow to score consistently, painful to audit, and nearly impossible to standardize interviews and reviews across engagements without rebuilding the wheel each time.

The Bespoke AI Solution

AI Risk Navigator is a FastAPI + React stack (Postgres, Redis) with an agentic assessment layer:

1. Multi-tenant model — Tenant → client → use case → versioned assessment rounds; row-level isolation at the database.
2. Bulk Excel import — Pre-fills and triggers branching across a 147-question assessment template; scores combine weighted, traffic-light, and LLM-adjusted signals.
3. Workflow & outputs — Mitigation planning, governance reporting, PDF/PPTX exports; LangSmith-ready observability and full audit trail.
4. Portals & auth — Entra ID for staff; magic-link JWT for vendor and client portals (Safe Links–aware reuse window).

The Client's Original Problem

A quantitative research team trying to read market sentiment from social was stuck manually searching X (Twitter), searching mostly on raw tickers that miss most of the conversation — then reconciling exports by hand. Keyword counts and overnight batches could not keep up with intraday moves or capture nuance (sarcasm, context, jargon).

The Bespoke AI Solution

Signalyze is a FastAPI + React (TypeScript) stack with background workers and Redis—built for continuous ingestion and scoring:

1. LLM search-term expansion — Generates platform-aware query phrases per symbol so retrieval goes beyond the ticker alone.
2. X fetch — Pulls recent posts from X; normalized storage with Redis caching and coordination.
3. LLM sentiment + workers — ARQ-driven async processing; confidence-weighted scores; embeddings (e.g. ChromaDB) for contextual chat over the corpus.
4. Live terminal UI — Server-Sent Events for streaming updates, Plotly charts, multi-symbol and multi-source views in the browser.