Custom AI that eliminates your most time-consuming manual workflows

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.

The Client’s Original Problem

The client's engineering teams face a flood of CVE and OSV advisories per repository. Triage is manual, patches are hand-written or copy-pasted, tests rarely run in the same pass as the fix, and turning a finding into a verified git commit or PR typically spans tools, people, and days—if it happens at all.

The Bespoke AI Solution

Rapid Shield combines scanning agents, a remediation orchestrator, and a graph-shaped pipeline (Pydantic Graph with typed nodes) behind a production API and dashboard:

1. CVEScannerAgent — Vulnerability detection aligned with OSV/CVE workflows; routes into remediation jobs you can track in the UI.
2. RemediationOrchestrator — Multi-phase agent loop: fix → test write → run tests → validate → commit, with specialized agents and tools (read/write files, run tests, syntax checks, git operations).
3. Graph orchestration — V2 state machine with feature-flagged graph mode: nodes such as fix, test pipeline, CVE verify, persistence for crash recovery on long runs.
4. Product surface — FastAPI backend, React frontend (scan forms, job history, evidence), JWT auth; GitHub service for PR-style handoff; Playwright E2E for full scan → remediate paths.

The Client’s Original Problem

Privacy teams drown in Data Subject Access Requests spread across email, CRM, and dozens of data stores. Manual collation of personal data is slow, error-prone, and risky—while proving CCPA compliance to auditors or courts from ad-hoc spreadsheets is nearly impossible.

The Bespoke AI Solution

DSAR Sentinel is a multi-service platform: FastAPI gateway, React admin and requester portals, and a stateful LangGraph workflow with per-agent services and human-in-the-loop interrupts:

1. Agent swarm — Orchestrator, verifier, federated searcher, redactor, responder, and compliance validator—each in its own container, coordinated through a checkpointed DAG.
2. PII Vault — Envelope encryption and tokenized PII so downstream LLM agents operate without raw personal data in context.
3. Federated discovery — Connector SDK with YAML manifests, policy-as-code topology, and a dedicated PII / dark-data scanner—wire only the systems you use.
4. Audit & output — Immutable audit ledger with LangSmith trace links; court-backed PDFs with QR + hash verification; model routing cascade for cost and quality.

Databases & data systems

• PostgreSQL or any SQL database — primary relational store and LangGraph checkpointing
• MongoDB or any document store — documents (e.g. support notes, attachments metadata)
• Qdrant or any vector database — vector search for semantic / embedding-backed discovery
• Neo4j or any graph database — graph-shaped data via the graph connector
• S3-compatible object storage — reports, files, and blobs

SaaS & API connectors

• CRM: Salesforce, HubSpot, Twenty CRM (open-source CRM stack in the demo harness)
• Commerce & payments: Shopify, Stripe
• Generic: REST connector for custom HTTP APIs; S3 for bucket-based sources

Configurable options

• Per-connector manifests — credential fields, capabilities, and discovery hints in YAML (admin UI and vault consume the same definitions)
• LLM backend — local Ollama (air-gapped) or OpenRouter; confidence-based model cascade across tiered open models
• Topology — enable or disable sources per tenant; policy-as-code over which systems participate in a DSAR
• Human-in-the-loop — LangGraph interrupts for high-risk steps before finalize / send
• Observability — LangSmith traces linked from the immutable audit ledger