Where AI stops being a demo and starts saving hours.

We work inside your business, find where people lose time or make weak calls, and build the AI workflows, agents and automations that fix it, measured on the hours and decisions they change.

A new role is exploding across AI: the forward-deployed engineer, the person who goes inside a company, learns how it actually works, and builds AI into the real workflow. It exists because the biggest blocker to AI is not the models, it is that no one has translated where the time really goes into a working system. That translation, from a messy, information-heavy business into a workflow AI can actually run, is the work. It pairs 12+ years reading institutional markets with 7+ years shipping production systems in Python (FDP), including SentiSift, an ensemble of 8+ AI models running live at 94% accuracy. What was built for five live products gets built inside your business, by Tom Pickel.

Who this is for

  • You have AI tools, but they sit unused and the hours you expected to save never showed up
  • Your team spends the day on manual reading, gathering, formatting and re-keying
  • You make high-stakes calls on information that arrives too slowly or too scattered to trust
  • You want AI inside your real workflow, not another subscription no one opens

What you get

  • A map of where your team loses time or makes weak calls, and where AI actually moves the needle
  • Working AI workflows, agents and automations built into your process, not a slide deck
  • Built in Python, delivered ready to run, documented and owned by you
  • Measured on the hours and the decisions it changes

How it works

  • We spend time inside the work and find the real bottleneck
  • We scope the smallest AI workflow that removes it
  • We build it, roll it out with your team and measure the impact

Common questions

What exactly is a forward-deployed approach to AI?

The work starts inside your business, not from a template. We learn how your team actually operates, find where the time and the weak decisions really are, and build the AI workflow around that. The label is new; the discipline, translating a real business problem into a working system, is what Pickel Fintech has always done.

Is this just wiring up ChatGPT?

No. Off-the-shelf models are a starting point, not the system. The work is the design and engineering around them: the data they read, the steps they run, the checks that keep them reliable and the workflow they live in. SentiSift runs an ensemble of 8+ AI models in production at 94% accuracy, which is the level of engineering behind this.

Do I need to be technical to start?

No. The whole point of the role is to bridge that gap. You bring the business and where it hurts; we handle the translation into a working system. Most of the value is in asking the right questions about your workflow, not in you learning to build.

Do I own what you build?

Yes. It is built in Python, delivered ready to run, fully documented and yours to keep, with no lock-in.

Related work: the production AI behind comment intelligence (SentiSift), and the systems this practice builds around it, investment data systems, operational data and process systems and custom data and automation engineering. See the full advisory practice.

Point us at the work that should already be automated.

Send a short note about where your team loses the most time or makes the shakiest calls. You get a clear read on what AI can actually take off your plate and a plan for the workflow that does it.

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