A small, focused practice working at the intersection of emerging technology and operational reality. Our flagship is agentic AI systems — agents that do the work autonomously, end-to-end. Around that: software engineering, data infrastructure, hardware and sensors, and the technical evaluation that ties it all together. For operating companies losing hours to manual work. For investors who need honest technical eyes on AI companies and portfolio efficiency.
Most engagements start at agents — they are the clearest way to turn hours of manual work into minutes of autonomous output. But the right answer isn't always an agent. Sometimes it's a custom internal tool. Sometimes it's a sensor pipeline, a data architecture, or an honest evaluation of someone else's tech. Working across the full stack means we can see what's actually needed — not just what we know how to sell.
Technical due diligence on AI companies, retained advisory on portfolio AI questions, and rapid deployments inside portfolio companies — all built on the same daily practice of shipping production systems.
Three voices from one engagement at a regional law firm. Same project. Three perspectives. Same realization.
A working snapshot of the tools we evaluate, build with, and integrate — across AI models, agent frameworks, software engineering, data infrastructure, and hardware. New options ship weekly. The list isn't a commitment; it's a signal that we stay current at the frontier so clients don't have to. The right tool gets picked for the job.
For operating companies: we find where time and money are being lost and build systems that fix it. For investors: we evaluate the technology, the team, and the moat — and ship rapid deployments inside portfolio companies. Both draw from the same daily practice of building production systems.
Custom systems built end-to-end — agentic AI, software, data infrastructure, sensor integrations. Scoped and priced upfront. Wired into the tools your team already uses; no behavior change required.
Retained technical advisor for ongoing questions — what to build internally, what to buy, what's worth piloting, what's hype. Real-time fluency at the frontier, available when needed.
Technical due diligence on AI companies for investors. Vendor evaluation for organizations choosing tools. Architecture review for teams building internally. Delivered as a memo with a clear recommendation.
Embedded engagement inside the team — usually 4 to 12 weeks. We sit alongside your engineers, ship code, transfer knowledge. Particularly valuable post-acquisition or for organizations standing up internal AI capability.
Every sector has the same underlying dynamic: highly capable people spending meaningful time on work that is repetitive, information-intensive, and below their level. The agent system is always different. The opportunity is always the same.
The law firm was the first deployment. The same architecture — agent teams with authenticated access to domain-specific data, delivered through interfaces people already use — applies across every professional services environment and most operational ones.
→ Don't see your sector here? If your organization has people doing repetitive research, document processing, or information work — it's worth a conversation. The evaluation tells us what's real.
Tillinghast Technologies is a small, focused practice working at the intersection of emerging technology and operational reality. We build software, agentic systems, data pipelines, and hardware-aware tools — and we evaluate the technology landscape for organizations that need clear answers, not vendor pitches.
The firm is built around a single principle: a working system that saves real time and real money is the only argument that matters. Whether the work is a multi-agent research tool, a custom data platform, an embedded sensor pipeline, or a technical assessment for an investment thesis — the deliverable is the same. Something that works, measured against something that did not.
Twenty-five years across design, engineering, and product — most recently at Microsoft and Meta, working deep in generative AI, machine learning, computational photography, computer vision, and the enterprise software underneath them. Career spans the full stack: visual design, product management, software engineering, ML, and applied AI systems.
What matters for the firm is the daily practice now. Multi-agent systems running in production, tested against real workflows with real users. When a new model or framework ships, he's in it the same week. That breadth — and the willingness to refuse to be just one thing — is what lets the practice see what's actually needed across software, hardware, data, and AI.
No intake forms. No deck requests. Reach out directly — we'll talk through the problem and whether we can help. Whether you're losing hours inside an operating company or evaluating AI at the deal or portfolio level, the conversation starts the same way.