Your best people lose their days to manual, information-heavy work. We redesign those workflows around AI so more of the work runs on its own, your output climbs and the cost behind each result drops.
Most businesses pay far too much for the output they get, because skilled people spend their days on reading, gathering, checking and re-keying that AI can now do well and cheaply. The real gains come from redesigning the workflow itself: AI does the interpreting, your spreadsheets, rules and code do the parts that have to be exact, and more of the work runs on its own while people step in only for judgment. It is the same engineering behind live products like SentiSift and Cuebit, backed by 12+ years reading institutional markets. Done right, your output rises while the cost behind each result falls.
Most AI advice comes from people who have never shipped a system that has to work every day. This practice has built the same thing four times over, taking messy, high-volume information and turning it into decisions and actions: SentiSift for reader discussions, Cuebit for a constant stream of sources, The 13F for institutional filings and Souppe for portfolio structure. Whatever gets built for you is delivered the same way, in Python, documented and owned by you, running inside the tools your team already uses.
The gains cluster in a handful of workflow types. If your business runs on any of these, there is real money in redesigning it around AI.
An operational process end to end: read the incoming material, classify it, check it, update the system, draft the response and escalate only the exceptions.
Invoices, statements, reconciliations and reports, where AI reads the messy input and your code does the exact maths and validation.
Watch every contract, holding or interaction around the clock, ignore the normal and surface only what needs a human.
Collect, connect and analyse large amounts of information into a conclusion your team can act on, kept up to date on its own.
Continuously find, qualify and rank opportunities and prepare the right approach, instead of just sending more email.
Add intelligence to what you sell, so the product decides or does part of what the customer used to have to work out alone.
The lowest-risk way to begin. A fixed-scope, fixed-fee first step that ends in a plan you can act on, whether you build with us or not.
Over about two weeks we spend focused time inside one part of your business, sit with the people doing the work, and map where the hours and the weak decisions really go. You walk away knowing exactly where AI pays off and what it is worth. If you move ahead with the build, the audit fee is credited toward it.
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Most engagements begin with an AI Opportunity Audit, a fixed-scope, fixed-fee first step over about two weeks. You get a workflow map, a ranked shortlist of where AI pays off and a build plan for the top opportunity. If you move ahead with the build, the audit fee is credited toward it.
The work starts inside your business. 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, but the discipline is not. Turning a real business problem into a working system is what Pickel Fintech has always done.
No. A single chatbot is rarely the right answer. We match the right model to each job: some run on your own hardware for privacy or cost, some in the cloud, at the level of sophistication the task actually needs. The real work is the engineering around them, the data they read, the steps they run, the checks that keep them reliable and the workflow they live in. SentiSift already runs a multi-model AI ensemble in production, and Cuebit turns a constant stream of sources into sharp briefs, which is the level of engineering behind this.
No. Bridging that gap is the whole point of the advisory work. You bring the business and the pain, and we handle the translation into a working system. Most of the value comes from asking the right questions about your workflow, and that part is on us.
Yes. It is built in Python, delivered ready to run, fully documented and yours to keep.
Related work: the production AI behind comment intelligence (SentiSift) and idea monitoring (Cuebit), and the systems this practice builds around them, investment data systems, operational data and process systems and custom data and automation engineering. See the full advisory practice.
Tell us where your team loses the most time or makes the shakiest calls. You get a clear read on what AI can take off your plate, and the option to start with a fixed-scope AI Opportunity Audit.
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