Forward-deployed AI engineering

We consult.
We build.
We enable teams.

Gerry Wolfe

Led by Gerry Wolfe, forward-deployed AI engineer and former attorney

15+ years practicing law · 10+ years shipping production software

Lambda Engine finds where AI pays off in your operations, ships production agent systems on your own cloud, and leaves your engineers fluent in the tools and practices to keep shipping without us.

Book a 30-minute AI readiness call See the airport case study
01

Consult

Map the workflow, find the ROI, scope a pilot your security team will sign.
02

Build

Agent systems on Claude, MCP and sub-agents, shipped with evals on Databricks or Azure.
03

Enable

Train your product team on Claude Code, skills, MCP and multi-agent workflows.
You own the code · Runs in your cloud · Based in Bismarck, ND
Currently at OpenAI, working on solving PII-related flows and inference
Former attorney, 15+ years
ODSC speaker & host
Best Presentation Award, ICOAI 2025
How we work

One partner from first conversation to a team that ships on its own

Most engagements touch all three. Start wherever you are: an idea that needs scoping, a pilot that needs to reach production, or a team that needs to get fluent in agentic development.

01 / CONSULT

Consult

Find the workflow worth automating before anyone touches a model.

  • Workflow mapping and measurable pain points
  • AI readiness and architecture review
  • Data, privacy and risk review for legal and regulated work
  • Written scope: pilot or full build, aligned to procurement

You leave with a scoped plan, a timeline and a success metric.

02 / BUILD

Build

Production agent systems, from proof of concept to deployment in weeks.

  • Agent systems built mostly on Claude, with the Claude Agent SDK and PydanticAI
  • MCP servers, sub-agents, agent skills and multi-agent orchestration
  • Evals and human-in-the-loop checks before anything ships
  • Deployed on Databricks or Azure, in your tenant

You get working software, full source code and the IP.

03 / ENABLE

Enable

Your engineers build alongside us, then keep going without us.

  • Hands-on training in Claude Code, agent skills and MCP
  • Multi-agent workflows for real product work
  • Pairing with your software team on live builds
  • Documented practices and a clean handoff

Your team keeps the skills, the tooling and the playbook.

The stack
Claude Claude Agent SDK MCP servers Sub-agents Agent skills Multi-agent orchestration PydanticAI Claude Code Evals Databricks Azure Azure ML
Featured work

Consult, build and enable, in one engagement

Client names are withheld. Each case study has its own page with the problem, the architecture and the results.

Flagship case study

Major US airport

Started as an MLOps build for traffic prediction. The client came back for a second engagement: airport operations tooling built with their software team, while we trained their product development team in agentic development and handed off the practices.

Consult Build Enable
Read the case study
Engagement 1 · Build

MLOps for traffic prediction

Camera-based computer vision models feeding traffic prediction, with training and deployment run through Azure ML pipelines.

Camera vision models Azure ML pipelines MLOps
Engagement 2 · Build + Enable

Airport operations tooling, built with the in-house team

Built operations tooling alongside the airport's software team, while training the product development team on agentic tooling.

Ops tooling Claude Code Agent skills MCP Multi-agent workflows
Handoff

Practices handed off, team shipping on its own

The working practices, tooling and conventions now belong to the client's teams.

A return engagement. The client brought us back after the first build to extend the work and upskill their own teams.

More case studies

Legal, insurance, privacy and security, built for high-volume operations.

All work

Resume Refinery

AI-powered resume reformatter that converts any candidate resume into a polished, Client-branded Word document in under a minute.

Headline result: ~85–90% time saving · 15 min vs 1.5–2 hrs per resume

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.

Results

Time per resume reduced from 1.5–2 hours to 15 minutes — an ~85–90% time saving with higher consistency and zero transcription errors.

Policy Burst

Policy Burst automatically transforms multi-form insurance policy PDFs into populated, ready-to-use Word document templates in minutes — eliminating days of manual data entry.

Headline result: 50+ hours → under 10 min per policy · ≥95% accuracy

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

Results

Processing time dropped from 50+ hours to under 10 minutes per policy (300-PDF batch: ~10 hours → ~40 minutes) with ≥95% accuracy.

Invoice Hub

Secure self-service billing portal replacing DOCX/email invoices—time entry, approvals, Excel to accounting, Azure OpenAI on line descriptions.

Headline result: V1 in active delivery · targeting ≥70% less manual handling

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.

Results

V1 in active client delivery. Replacing manual re-keying and email-based validation with a single portal path from time entry to accounting — targeting ≥70% reduction in manual handling within two months of launch.

AI Maturity Assessment

Public web assessment across six AI maturity dimensions—benchmarked reports, gap analysis, phased action plan via Azure AI.

Headline result: 10–15 min questionnaire · report in 30–90 s

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.

Results

Questionnaire: ~10–15 minutes. Full AI maturity report: from weeks of manual consulting synthesis to 30–90 seconds—benchmarks, gaps, and phased plan.

AI Risk Navigator

Excel-fed, multi-signal AI risk assessments on a 147-question legal template—tenant, vendor, and client portals.

Headline result: Architecture validated · phased rollout with client

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).

Results

Architecture validated with the client; phased rollout underway. From days of spreadsheet coordination to under 60 seconds per use case — auditable, legal-review-ready output from import to triple-scored assessment.

Signalyze

Multi-source stock sentiment terminal—LLM-generated search terms for X, confidence-weighted sentiment scoring, and real-time dashboards with SSE streaming.

Headline result: One pipeline from social firehose to live charts—not overnight CSVs

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.

Results

From stale spreadsheets and site-hopping to streaming sentiment dashboards fed by one automated pipeline—cross-platform correlation and drill-down without maintaining manual keyword lists per stock.

Rapid Shield

AI-powered CVE scanner and remediation stack—OSV-backed detection, agentic fix → test → validate → commit loop.

Headline result: End-to-end runs from vulnerability report to patched repo—not weeks of ticket back-and-forth

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.

Results

From disconnected advisories and manual patches to orchestrated remediation runs with automated tests, validation, and git-backed outcomes—plus structured logging for evaluation and LLM-as-judge review.

DSAR Sentinel

Production-grade CCPA DSAR automation—a LangGraph agent swarm covers intake, identity verification, federated PII discovery, redaction, compliance validation, and court-backed PDF reporting.

Headline result: Full lifecycle from request to defensible response—agents never touch raw PII in the clear

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

Results

From email threads and one-off exports to orchestrated DSAR runs with redacted bundles, compliance checks, and tamper-evident reporting—built for teams that need to stand behind every response.

FREE ASSESSMENT

Not sure where AI can help your team?

Take our free 2-minute AI Readiness Assessment. Get instant scores across 8 dimensions and see exactly where automation can make the biggest impact.

Take the Assessment
Why teams hire us

Why Choose Lambda Engine

Bespoke solutions with zero vendor lock-in

We build custom AI tools that fit your exact workflows — no generic platforms, no ongoing licensing fees, and full ownership of the code and IP.

Dramatic efficiency gains

Turn hours (or days) of manual work into minutes. Our clients routinely see 85–90% time reductions on repetitive tasks while eliminating transcription errors.

Cost-effective ROI from day one

No bloated enterprise software budgets. We deliver targeted AI that pays for itself quickly through measurable productivity and error reduction.

POC to Production in weeks

weeks, not months

We don't leave you stuck at a proof-of-concept. Every solution is designed and delivered as secure, scalable production software on your infrastructure, ready to ship in single-digit weeks.

Enterprise-grade, yet instantly usable

Delivered as a web app, Slack bot, API, MCP server, Microsoft Teams bots, or CLI tools — all secured with your own infrastructure and your existing credentials. No new logins, no training required.

Human-in-the-loop intelligence

AI handles the heavy lifting, but we keep you in control with validation loops, confidence scoring, and smart flags so nothing slips through the cracks.

Gerry Wolfe
THE FOUNDER

Gerry Wolfe

AI Engineer/Builder combining 15+ years of legal practice (JD, former managing attorney) with a Master's in Computer Science and a decade of production software engineering. Architect of multiple deployed agentic AI applications for enterprise legal clients; published researcher in semantic reasoning and LLM grounding. Equally fluent in courtroom logic and cloud infrastructure.

Currently at OpenAI, working on solving PII-related flows and inference. Previously architected Gen-AI RAG systems at HubSpot, CVS Health, and other enterprises. Published research includes ReasonDB (semantic reasoning database, Best Presentation Award ICOAI 2025) and Tarski-LLM (factual grounding in LLMs).

JD + MS Computer Science
Published AI researcher
Open source contributor
15+ years legal practice
10+ years engineering
Agentic AI specialist
LinkedIn GitHub Take a Free AI Readiness Assessment
Security practices

Built for sensitive data from day one

How every build is designed, whatever your industry.

Private, permission-aware deployments

Agents see only what each user is allowed to see

Runs in your cloud / VPC

Your tenant, your network, your existing sign-on

PII redaction and audit trails

Sensitive fields masked; every action logged

Human-in-the-loop review

People approve high-risk steps before they run

Evals before every release

Accuracy measured, not assumed

You own the code

Full source and IP, no vendor lock-in

Speaking

On stage, on the mic, and hosting the room

Talks and hosting on agent systems, reasoning and AI for legal and forensic work.

ODSC

Open Data Science Conference

Techno Security

& Digital Forensics

ODSC

ODSC East 2025

Presented ReasonDB

Speaker
ODSC

ODSC West 2025 · San Francisco

Emceed conference tracks

Emcee
ODSC

ODSC Agentic AI Summit

Hosted · July 2026

Host
ODSC

ODSC.ai podcast

Guest host · Aug 2026

Guest host
TS

Techno Security West 2026

Techno Security & Digital Forensics Conference

Speaker
★

Best Presentation Award, ICOAI 2025

Research behind ReasonDB

Award
Invite Gerry to speak
Writing · Pinned essay MAY 2026 · SUBSTACK

Maybe Buy the Platform. Definitely Build the Agent

The context architecture for agentic AI.

Read the essay

Field notes from the build

Essays on agents, evals, context and AI for lawyers.

gerrywolfe.substack.com

Subscribe on Substack
We build products too

EvidenceThread

Forensic case management for defense teams. Organize evidence, track discovery, and build your theory of the case in one place — built by a former attorney who knows the work.

Visit evidencethread.com

evidencethread.com

Illustrative interface shown. Product in active development.

Skills library · Early access

Agent skills for criminal defense teams

Ready-to-run agent skills built by a former attorney for the work defense teams actually do. Early access is open: request an invite and we'll set up your team's sign-in.

Request an invite

Discovery Review

Work through large discovery productions: sort, summarize and flag what matters to the defense.

Early access

Witness Impeachment Prep

Line up prior statements and testimony to surface inconsistencies before cross-examination.

Early access

Ready to cut your heaviest manual workflow?

Describe the workflow. We'll propose scope, timeline, and a clear path from problem to POC to production on your infrastructure stack.

Most work starts with a discovery call, then a written scope—pilot or full build—aligned to your security and procurement process.

Book a discovery call

Prefer to write first? gerry@gerrywolfe.com