Direct access, no handoffs
NLT Labs is technology and AI consulting by Bill Thornton, a SaaS technology executive with more than 25 years of experience leading engineering, cloud platforms, acquisitions, security, compliance, and enterprise transformation.
Who we help
Mid-market and growth-stage teams where individual AI use is high, but the company program is still a proof of concept. The blocker is rarely access to models. It's structure, integration, ownership, and someone who has carried this work before.
The proof of concept looked great in a sandbox. Months later it's still a POC: no evals, messy integrations, and nobody owns what happens when the model gets deprecated.
Employees reach for personal ChatGPT or Claude because sanctioned tools are slower or stuck in pilot. Work gets done. Confidential data leaves on copy-paste. IT finds out later.
AI generates an answer. Someone copies it into the CRM, ticket queue, or spreadsheet. That's not a workflow. It's a tax on every person who touches the output.
SOC 2, ISO 27001, customer contracts, responsible AI policies. Mid-market companies face enterprise audit questions with a fraction of the staff.
Consulting services
Years leading AI and platform programs at enterprise scale: board readouts, compliance reviews, multi-team rollouts, production agentic systems. Mid-market companies don't need a 200-person transformation office. They need someone who's done this before to walk alongside their team.
Inventory what's already running on desktops and design company paths that match the speed people expect. Sanctioned tools, clear data rules, workflows wired into your stack.
Integration contracts, LLM-as-judge evals, error handling, and ownership when models deprecate. Discipline without the six-month architecture review.
Connect AI outputs to CRM, ERP, ticketing, and document stores so people stop copying answers between tabs.
SOC 2, ISO 27001, and FedRAMP patterns scaled down to what a 50- or 500-person company needs. Procurement-ready evidence from week one.
Embed as fractional CTO or implementation partner until your people own what you built together.
Pick a small, coherent stack. Say no to the rest. Tool sprawl is the silent budget leak.
How we engage
Honest current-state audit and a 90-day plan with owners, not a deck that disappears after the meeting.
Agents, RAG, document intelligence, and copilots that call real tools and fail gracefully. Evals from day one.
CTO-level judgment without full-time headcount. Org design, delivery, modernization, security and compliance, and where AI genuinely helps.
Agentic patterns, eval discipline, and production judgment so capability stays when the engagement ends.
Proof of work
None of this is client work. It's our own company, running on agents we built: 166 on one platform, wired into 36 production workflows, and they've run nearly twelve thousand times. The count isn't the point. We've already hit the problems you'd hit in month three. Figures read live off the platform on 7 August 2026.
Agent Stack · platform
The runtime everything else registers into, with its own library of expert reviewers for code, security, architecture and observability. A request gets matched to an agent, composed into a system prompt, and executed under a budget cap and an explicit tool grant.
Agent Stack · platform
Cross-session recall with hive propagation and a knowledge graph, reached over HTTP rather than embedded inside each consumer. Without a memory authority every agent starts from zero on every run. We built it because we hit that wall ourselves.
Agent Stack · platform
Agents that write documents. Our technical deep dives are built by it, from repo source, on a template. Nothing gets hand-assembled in a design tool, so a forty-page reference rebuilds itself when the code underneath it changes.
Portfolio Suite · products
Our biggest fleet: thirty-nine agents behind a capital gate. An intake doc runs an 11-step evaluation covering market, feasibility, financial model, regulatory exposure and a devil's advocate pass, and comes out as FUND, PASS or DIG DEEPER. On FUND, a 22-step workflow builds and deploys it.
Portfolio Suite · products
Fourteen intake pipelines, one per source: Reddit, YouTube, Hacker News, Product Hunt, Indie Hackers, Flippa, public procurement records and the open web. Seven agents do the work and get reconfigured per source, which is the whole trick.
Reference build · not sold as a product
A whole storefront operation, broken into five jobs a small business actually recognises. First commit was 28 July 2026, and by 5 August the same kit was running a second, independent storefront. That gap, eight days, is the honest answer to whether this transfers.
Catalog and listings
Probes the live storefront, extracts product data, drafts the listing copy and writes it back to the store.
Multi-channel marketing
Pulls social signal, plans a calendar, drafts the posts, then checks each for originality and disclosure before it publishes.
Customer service
Reads the ticket queue and drafts replies, holding every one behind a judge before a customer sees it.
Analytics and anomalies
Pulls orders, ad spend and site analytics, correlates them, then explains what moved instead of just charting it.
Go-live gate
Scores whether an agent is fit to run at all. Nothing reaches a customer without clearing it first.
Nine of the thirty agents do nothing but check the other twenty-one. Five judges cover claims, disclosure, originality, replies and visuals, three domain experts cover brand, commerce and compliance, and one scans for prompt injection. Roughly a third of the fleet exists to catch the rest, and that's what makes it safe to point at a live storefront.
The same thing works in your company. An agent is only worth running once somebody has decided what it's allowed to touch, what it's allowed to spend, and how you'd find out it went wrong. That's the unglamorous part, and it's most of the work. If there's a process in your business that eats a person's week, that's where we'd start.
Selected experience
Anonymized where needed. These results are from Bill's work as a technology executive before NLT Labs, not claims about NLT Labs client engagements.
Actual result · prior executive role
Helped scale a global enterprise SaaS business from approximately $24 million to $60 million in ARR while expanding engineering, infrastructure, security, and operational capabilities.
Actual result · prior executive role
Led technology and organizational integration across four acquisitions, balancing product continuity, shared platforms, infrastructure consolidation, and team alignment.
Actual result · prior executive role
Directed cloud, security, and compliance programs supporting SOC, ISO 27001, FedRAMP, and demanding enterprise and government customers.
Operating model
Designed and ran operating models spanning architecture, delivery, DevOps/SRE, quality, data, and ML/AI so growth did not outrun the systems that sustain it.
Why NLT Labs
In the codebase, the architecture review, and the exec readout. Real deliverables, weekly check-ins, direct access to Bill.
What procurement and security need, delivered at mid-market speed.
Selective engagements only. If we're not the right partner, you'll hear it in the first call.
How we work
Where you are, what you've tried, and whether NLT Labs is the right fit. No pitch deck.
Stack, workflows, and blockers assessed. Scoped proposal with timeline, owners, and measurable outcomes.
Build with your team, not around them. Your people own what lands when the engagement ends.
Who leads the work
Bill Thornton
Founder, NLT Labs · Technology executive & hands-on CTO
Bill Thornton has spent more than 25 years building and scaling enterprise software organizations. His experience includes SaaS growth, engineering transformation, cloud modernization, acquisition integration, security and compliance, and practical AI adoption.
Through NLT Labs, Bill works directly with leadership teams that need experienced technology guidance without a large consulting firm or unnecessary overhead.
Next step
Thirty minutes, no obligation. Whether you're scoping real work or you just want a second opinion before a decision, it's a straight conversation either way. If we're not the right people, we'll say so and point you at who is.
Prefer to write? hello@nltlabs.ai · About Bill