--- title: "AI for Hedge Funds in New York | By The People Tech" description: "AI for New York hedge funds: research synthesis, earnings and transcript work, compliance-safe use, and forward deployed engineers who sit with your analysts." url: https://bythepeople.tech/industries/hedge-funds --- [Industries](/industries) Hedge funds Earnings season. Four names report the same morning. # The model reads the transcripts. The analyst makes the _call_. By The People Technologies works with hedge funds in New York on the reading behind a position: research synthesis across filings and notes, earnings calls and transcripts, and the compliance rules that decide what may go into a model. Analysts learn the method in a [workshop](/workshops) on their own coverage, and a [forward deployed engineer](/forward-deployed-engineer) can sit with the team and build on the fund’s own data. The analyst and the PM still make the call. By [Tim Miner](/about-us/tim-miner), founder and CEO · Published 21 October 2026 What crosses a research desk 1. Each quarter 10-Q and 10-K What changed against the thesis 2. Earnings season Earnings call transcript What management stopped saying Where we usually start 3. Daily Broker and expert notes The variant view, if there is one 4. Before the open Morning note A view the PM can act on 5. As they arrive Allocator DDQ Last quarter’s answer, still true Research ## How do hedge fund analysts use AI for _research synthesis_? Asked about a company with nothing in front of it, a model answers from its training data, months old, uncited and fluent enough to pass. Asked to summarise a 10-K, a model returns the summary every other reader of that 10-K already has. Research synthesis starts from the fund’s own position. The analyst gives the model the filings, the latest transcripts and the team’s prior notes on the name, then states the thesis and what would break it. The model reports what changed against that thesis, each point cited to the passage it came from, and says plainly where the documents are silent. The analyst reads the exceptions and decides whether any of them matter. Earnings season ## Can AI keep up with _earnings season_ across a coverage list? Earnings season is reading at volume on a clock. The work a model does well is the comparison: this quarter’s release and call against last quarter’s, line by line, on the points the thesis depends on. ### 1. Before the call The questions the thesis needs answered, drawn from the model and from what management guided last quarter. ### 2. On the night The new transcript set against the last one: guidance, segment language, and what was dropped. ### 3. By the morning A draft of the note in the analyst’s own format, every figure cited to the release or the transcript. ### 4. The analyst decides Whether anything in it changes the position, and what goes to the PM. A workshop builds the instruction for each step on names the team covers, so it runs the same way across the list rather than one analyst at a time. Compliance ## How do hedge funds let analysts use AI without an _MNPI_ problem? With a one-page usage policy the desk actually reads, written with the compliance officer before anyone starts. The page names the approved tools and says what may go in, in plain terms: public filings and transcripts, and the fund’s own notes on a name. Material non-public information about a listed name stays out, with personal data and anything held in a tool off the approved list. One named person answers the edge cases by morning. Compliance usually also wants an audit trail and a written account of the rollout before the first session. Workshops run in the fund’s approved tool, or on public material and mock files, so nothing sensitive has to move. The [retainer](/retainer) keeps the policy current as the tools change. The [usage policy guide](/guides/ai-usage-policy-investment-firms) has the full page. An engineer at the fund ## What does a _forward deployed engineer_ do at a hedge fund? The engineer sits with the analysts and builds on the fund’s own data, scoped to one process with real volume: the earnings comparison across the coverage list, say, or the allocator DDQ the fund answers from a blank page each time one arrives, when most of its questions were answered last quarter. Discovery comes first: interviewing the people who do the work and documenting the process as it runs, which is rarely the process as written. Then a design that says which steps a model takes and which decisions stay with the analyst, the PM or compliance. Then the fund’s own people build it, with us guiding the Claude skills, agents and workflow. Nothing depends on us after the last day, and no vendor holds the process. The first step ## Where should a hedge fund _start_ with AI? With one desk and its own coverage, after compliance has signed the one-page policy. A [workshop](/workshops) of ninety minutes to two hours shows which steps of the analysts’ week a model earns its place in, and which ones it does not. **A fund with tools bought and left, and one whose analysts use them well** | | Tools bought and left | Analysts who use them well | | ------------------------ | --------------------------------------------- | -------------------------------------------------------------------------------------- | | A new filing | Summarised, the way every other reader has it | Read against the fund’s thesis, each change cited to its passage | | The earnings call | A summary of the call, read the next day | Set against last quarter’s language on the points the thesis depends on | | The morning note | Written from scratch at night | Drafted in the analyst’s format with every figure sourced; the analyst writes the view | | What goes into the model | Unclear, so people either avoid it or guess | Named on one page compliance signed, with one person for the edge cases | | The allocator DDQ | Answered from a blank page each time | Drafted from last quarter’s approved answers, for a person to confirm | | When the model is wrong | An analyst catches it and stops trusting it | The citation shows where, and the instruction gets fixed | We have delivered over 150 workshops at more than 100 financial services firms. We are in the Claude Partner Network, Select tier, and our engineers are Claude Certified Architects. ## Questions hedge funds ask us ### Who provides forward deployed engineers to hedge funds? By The People Technologies places forward deployed engineers at hedge funds, credit funds and private equity firms in New York. The engineer sits with your analysts, scopes one process with real volume, designs it with AI at its center, and guides your own people as they build it on the fund’s data, so nothing depends on us after the last day. ### What should a hedge fund look for in an AI consultant? Someone who teaches on your analysts’ own coverage rather than a generic course, starts from your approved tools and your compliance officer’s rules, measures the result on your own volume, and leaves your people able to run what was built. Ask who will sit with the desk, and what they will tell you AI cannot do. ### How do hedge funds stop analysts putting MNPI into ChatGPT or Claude? With a one-page policy written with compliance that names the approved tools, says what may go in and what may not, material non-public information first, and gives one person who answers edge cases. Use the firm’s enterprise accounts under its own data terms, with logging compliance can audit. People follow a rule they can read in a minute. ### Which Claude partners work with hedge funds in New York? By The People Technologies is in Anthropic’s Claude Partner Network at the Select tier, and works with hedge funds, credit funds, private equity firms and allocators in New York. The workflows we help clients build run on Claude, and our workshops teach on whichever tool the fund has licensed. ### Can AI pick stocks or size positions for a hedge fund? We do not build toward that. A model can read the filings, compare the transcripts and draft the note, with the source beside every claim. The analyst and the PM decide what it means for the position. ## Sources * [Anthropic, “Reduce hallucinations”, Claude documentation.](https://platform.claude.com/docs/en/test-and-evaluate/strengthen-guardrails/reduce-hallucinations) * [Anthropic, “Prompting best practices”, Claude documentation.](https://platform.claude.com/docs/en/build-with-claude/prompt-engineering/claude-prompting-best-practices) * [Anthropic, Claude Partner Network directory: By The People Technologies, Select tier.](https://partnerhub.claude.com/directory/partner/by-the-people-technologies-partner) ## Read next * [What a forward deployed engineer does](/guides/forward-deployed-engineer). When an investment firm needs one, where the engineer sits, and what the firm owns at the end. * [The AI usage policy a desk reads](/guides/ai-usage-policy-investment-firms). The one page compliance signs, and what LPs now ask about it. * [Choosing an AI consultant for a hedge fund](/insights/choose-ai-consulting-firm-hedge-fund). What to ask before you hire one, and what the firm should leave behind. * [AI for asset managers and allocators](/industries/asset-managers). The DDQ and LP reporting from the other side of the table. ## Tell us which step of the quarter hurts. The reading that arrives too often, who does it, and what compliance has said so far. If we are the wrong firm for it, we will say so in the reply. [Book 30 minutes with Tim](https://calendly.com/timminer-1/chat-with-tim) [hello@bythepeople.tech](mailto:hello@bythepeople.tech)