Sieve Combines AI and Human Review to Automate Data Accuracy at Scale

In the high-stakes world of hedge funds and investment firms, accuracy, speed, and efficiency are non-negotiable. Yet one of the most critical foundational processes — data cleaning — remains shockingly manual. Financial analysts and data engineers often find themselves combing through spreadsheets, reviewing PDFs, translating foreign documents, or even reviewing emailed data by hand. This is not a matter of preference but necessity: accuracy requirements in finance are extreme, and traditional AI systems still fall short of meeting them without human intervention.

Sieve was born to address this glaring inefficiency. The startup offers a smart, integrated solution to automate data cleaning — one that combines the power of AI with the precision of human review. Their product is simple: deliver clean, trustworthy data directly into the tools analysts already use, whether that’s a development pipeline via API or a financial model in Excel.

By targeting the "last mile" of data accuracy, where AI systems still require human validation, Sieve has created a product that’s not only technologically impressive but also immediately impactful for its users.

Who Are the Founders Behind Sieve?

Sieve is co-founded by Nicole Lu and Savannah Tynan, two professionals with deep roots in both finance and applied technology.

Nicole brings experience from Citadel, where she worked across Equity Quant Research, Global Trading, and Data Science. Her background also includes time at McKinsey as a management consultant, and even medical research, where she developed cancer detection algorithms at the Broad Institute. Her exposure to both precision-demanding fields and quantitative research environments gives her a unique perspective on the data challenges hedge funds face.

Savannah, Sieve’s CTO, previously worked at Bain & Company in the private equity group and participated in a high-growth startup externship where she helped scale revenue from zero to $10M ARR in just six months. A graduate of MIT with experience in applied machine learning, Savannah brings the technical horsepower to make Sieve’s ambitious vision a reality.

Together, the founders combine deep domain expertise, high-tier consulting and engineering acumen, and a firsthand understanding of how broken data workflows are in finance.

How Does Sieve’s Technology Work?

Sieve is built on a deceptively simple but powerful concept: integrate AI with human oversight to return fully cleaned, ready-to-use data directly into the user’s workflow.

The startup offers two primary modes of access:

API Integration for developers and engineers who need clean data injected directly into backend systems or automated pipelines.
Excel Integration for analysts who work in spreadsheet-based models and want a frictionless way to import cleaned data directly from URLs, PDFs, filings, images, or even documents in foreign languages.

Under the hood, Sieve’s process starts with AI models that search, retrieve, and extract data from a wide variety of sources. But unlike most AI tools that stop there, Sieve routes the extracted data through a network of expert human reviewers. This hybrid approach bridges the gap between AI’s speed and human-level accuracy, particularly vital in financial scenarios where even a single-digit error can cost millions.

This design not only meets the rigorous standards of institutional investors but also does so while being intuitive and flexible for end users.

Why Is This Problem So Critical in Hedge Funds and Investment Firms?

To outsiders, it might seem shocking that highly paid data engineers at top-tier hedge funds spend large chunks of their time cleaning and verifying data. Yet this is the norm. Even basic financial metrics like earnings dates or revenue disclosures can involve hours of manual verification due to inconsistent formatting, multiple languages, or contradictory data sources.

The financial sector’s high dependency on accuracy, combined with fragmented data sources, has created a situation where under-leveraged talent is doing menial but necessary work. Outsourcing or using low-accuracy AI systems hasn't solved the problem — and often creates new ones.

Sieve steps in to alleviate this pain point. By replacing manual workflows with programmatic, scalable, and accurate systems, Sieve enables financial teams to:

Free up engineering talent for more impactful work.
Reduce turnaround time from days to minutes.
Improve accuracy and compliance without internal QA bottlenecks.

In short, Sieve turns data cleaning from a bottleneck into a background task.

How Does Sieve Perform Compared to Existing Solutions?

From the earliest days of its launch, Sieve demonstrated its competitive advantage in measurable terms. In client tests, the company achieved:

100% match rates compared to manually hand-collected datasets.
Weeks of manual work reduced to passive automation, dramatically boosting internal productivity.
60–70% cost savings compared to traditional methods, including BPO-based manual review teams.

These are not marginal gains — they’re transformational. Whether viewed from a cost, accuracy, or scalability perspective, Sieve has managed to outperform every conventional method while being easier to integrate.

What Makes Sieve’s Excel Integration So Unique?

While APIs are ideal for dev-heavy organizations, Excel remains the tool of choice for many financial analysts. Sieve has recognized this reality and built an integration that allows analysts to import cleaned data into their spreadsheets, with just a URL.

What makes this even more powerful is the range of data sources Sieve supports:

Filings (e.g. SEC 10-K, 10-Q)
PDFs
Scanned documents and images
Non-English text sources

This level of flexibility, combined with data reliability, means analysts no longer have to copy-paste data manually or double-check AI output. Instead, they can focus on making investment decisions, confident that the data feeding their models is accurate and vetted.

Why Is Human-in-the-Loop Essential in Financial AI?

One of the most important lessons Sieve internalized early on is that AI alone is not enough. Especially not for hedge funds, where precision is paramount.

Traditional AI data extraction tools often boast high recall and speed, but fail to meet the accuracy standards of financial modeling. As a result, analysts still find themselves reviewing AI-generated outputs manually, defeating the purpose of automation.

Sieve’s innovation lies in integrating a “human-in-the-loop” (HITL) step that reviews and corrects AI output. This allows for:

Auditable, compliant data trails
Reduction in false positives and negatives
Peace of mind for both analysts and compliance teams

By blending AI speed with human accuracy, Sieve has created a feedback loop that gets better over time while staying trustworthy from day one.

What’s Next for Sieve?

Having already proven itself with early adopters during Y Combinator’s Spring 2025 batch, Sieve is poised to scale. The team is focusing on expanding their expert reviewer pool, refining AI models to handle even more complex data types, and deepening integrations into popular financial tools and platforms.

Future plans may also include:

Support for real-time streaming data
Integrations with other data platforms (Snowflake, Tableau, etc.)
Compliance tools and data lineage tracing

The goal is clear: to become the default data cleaning layer for the financial sector — the invisible infrastructure behind high-quality analysis.

Why Should the Financial World Pay Attention to Sieve?

In an industry where milliseconds can affect millions and one erroneous data point can trigger catastrophic losses, data quality is not optional — it’s existential.

Sieve offers a unique and elegant solution: accurate, scalable, and user-friendly data cleaning that slots directly into existing workflows. Whether through an Excel sheet or a JSON API, Sieve empowers analysts and engineers to stop worrying about data prep and get back to doing what they do best — making better financial decisions.

With a team rooted in the reality of finance and the rigor of technical problem-solving, Sieve isn't just solving a problem — it's eliminating an entire category of operational friction.