Technical strategy & modernization consulting

Software evolves. So should the way you think about it — with help from someone who's seen the last few cycles.

Senior technical consulting for organizations modernizing systems, integrating AI thoughtfully, and making architectural choices that benefit from experience.


What I do well is see the whole picture — and help you find the clearest path through it.

Legacy System Modernization

Your system has value — it runs your business. The question is how to evolve it safely. I map what you have, find the best sequence for change, and build a phased plan that protects what's working while making room for what's next.

Architecture review Systems assessment Technical debt Migration planning

Fractional CTO & Technical Leadership

Some organizations need a CTO five hours a week, not forty. I'm there for the architecture calls, the hiring decisions, the AI questions that keep coming up, and the conversations where someone needs to say "here's what I'd actually do" — then I step back until the next one. The arrangement scales to fit the company, not the other way around.

Strategic oversight Architecture decisions Team mentorship Vendor evaluation Specialist coordination

AI Integration, Done Thoughtfully

I've worked on production AI integrations across more than a decade — IBM Watson NLU for classification, AutoAI for ranking, the Claude API for lead pre-processing, and contributed to voice AI for an answering service. I've also retired AI services when they stopped earning their place. The goal is practical efficiency — real workflows that save your team real time — and I'll be honest about where AI fits your situation and where it doesn't.

Workflow automation LLM integration ML model training Operational tooling

Cloud & Serverless Architecture

Over a decade of production AWS — Lambda, DynamoDB, API Gateway, and more — has taught me to design serverless systems that stay clean and maintainable as they scale. The best architecture is the one that still makes sense in three years, at ten times the traffic.

AWS serverless API design Infrastructure review Scalability planning

I like to understand a system before I suggest changing it.

Good technical decisions come from clarity — about what you have, where you're headed, and what's actually involved in getting there. That clarity doesn't appear on its own. It takes a deliberate first step.

Every engagement begins with a paid discovery phase: a structured assessment that produces a real deliverable — your architecture mapped, your priorities organized, and a phased roadmap you own outright. That document is useful whether or not we continue working together, and it's the most grounded way to start.

After discovery, we work in phases. Each one has a clear deliverable and a natural stopping point — you continue because the work is proving its value, not because you're locked in.

Assess before acting

Every system has a story its documentation doesn't fully tell. The assessment brings the real picture into focus.

Stabilize, then modernize

Strengthen what's running today first. A stable foundation gives you the confidence to evolve at whatever pace makes sense.

Phased, transparent delivery

Each phase stands on its own. You always know what you've received, what it's worth, and what comes next if you choose to continue.

Plain language, always

Communication isn't a soft skill. It's an integral part of technical work. If I can't explain a recommendation in terms a non-technical stakeholder can act on, I haven't finished thinking it through.



Twenty-seven years of building software — and learning what makes it last.

I wrote my first production code in 1999, for an ad agency that wanted to push the boundaries of what the web could do. I've been a senior engineer for seventeen of the twenty-seven years since. I've shipped games, dynamic websites, content management tools, reporting systems, hybrid mobile apps, serverless platforms, and most recently, AI-driven systems. Each generation of languages & technology arrived promising to make the previous one obsolete, and each one quietly turned into infrastructure the next generation built upon. In my time I've watched paradigms come and go — and I've helped the systems that had to survive each transition.

For most of the past seventeen years, I've done that work at a single company that was built around a website I developed as a contractor with the marketing agency Wheelhouse Strategy — a site that produced a roughly sevenfold business surge for their client in the month after launch, prompting the site owner to launch a company around the model. The work that began as a single site became the company; the company became the platform; the platform produced Sherloq, an AI-driven serverless analytics app on AWS that earned IBM Partner status and was featured as an IBM case study. Along the way I helped integrate several different production AI services — IBM Watson NLU, IBM AutoAI, the Claude API, and contributed to a Vapi voice AI for after-hours call intake — and acquired the discernment to know when to retire an AI service that has stopped earning its place.

I've also learned, across many years, that a meaningful share of senior technical work isn't technical — it's noticing the structural friction a team has been working around, and solving those bottlenecks. The product specs that didn't exist. The handoff plan between departments. The meeting notes nobody was taking. The technical voice in client meetings, translating engineering decisions clearly, without condescension.

My job is to tell you what I'd do, and why. If I think a direction is going to cause real harm, I'll say that clearly, once, in writing. After that, my job is to help you execute the path you've chosen as well as it can be executed — not to keep relitigating the decision.

Before all of this, I studied the arts — music, film, photography, and martial arts. It may seem unrelated, but it turned out to be the part of my training that taught me to pay attention and listen, to communicate effectively, and to prefer elegant solutions over elaborate ones.

I work from a geodesic dome in a forest, on a mountain, in western Massachusetts. It's an expansive, creative, and quiet space. It turns out, that makes a difference.

AWS / Serverless Node.js / TypeScript React / Angular / Nest.js LLM Integration ML model training DynamoDB / PostgreSQL / MySQL DevOps Technical documentation

Software that has to keep running.

Most of what I do well, I learned by doing it for a long time on systems that had to keep running. Here's the work that shaped how I think.

Sherloq — AI-enabled analytics platform

Co-led architecture and development from 2015 onward. The work earned IBM Partner status and was featured as an IBM case study.

Stack

Serverless AWS Lambda DynamoDB API Gateway Cognito IBM Blockchain Node.js / TypeScript React

Production AI integrations

  • IBM Watson NLU lead classification In production
  • Claude API incoming lead pre-processing In production
  • IBM AutoAI ad ranking Retired, when it stopped earning its place

Modernization approach: risk first, then end-of-life dependencies, then frequency of use. Stable surfaces stay stable. Everything else has to earn the change.

Voice AI for answering service

Contributed to a Vapi-based voice AI system that handles calls for businesses. Still in production

Tell me about it.

The first conversation is free and genuinely no-pressure — really just a chance to understand your situation and see whether what I do is what you actually need. If it is, I'll propose a discovery engagement with clear scope and a fixed fee. If it isn't, I'll tell you that, and I'll point you somewhere useful, if I can.

Aleda Jonquil
Northfield, MA