Executive Summary
Manual compliance reviews are a known bottleneck in financial services. For teams managing high volumes of investment video content, the process is slow, inconsistent, and expensive. BerryPie, a fintech startup founded by veterans of regulated finance and reg-tech, set out to fix that.
They came to Aegasis Labs with a clear vision and a tight deadline: build a production-ready AI compliance platform from scratch in 40 days. No shortcuts, no compromises on accuracy or security. Just a working product that compliance teams could actually use.
We delivered. In 40 days, BerryPie went from concept to a live MVP capable of ingesting investment videos, transcribing speech, analyzing content against compliance rules, and returning a scored, audit-ready report. What had previously required hours of manual effort now happens automatically, with clear rationale attached to every finding.
About BerryPie
BerryPie is a fintech startup on a mission to make financial compliance faster and more reliable. Their target customers are asset managers, brokerages, and research teams — organizations that produce significant volumes of investment video content and carry real regulatory exposure if that content isn’t reviewed properly.
The founding team brings direct experience from regulated finance and regulatory technology. They’d spent years watching compliance teams struggle with the same problem: too much content, not enough time, and a review process that relied almost entirely on human attention to catch every potential violation.
That firsthand exposure gave BerryPie both the problem clarity and the domain knowledge to design a solution worth building. What they needed was an engineering and AI partner who could take that vision and turn it into production software — fast.
The Challenge
A Broken Process Hiding in Plain Sight Financial firms produce thousands of investment videos every year. Market updates, product explainers, advisor commentary, research summaries. Each one carries regulatory exposure. Each one, under current industry practice, requires a human reviewer to watch it, transcribe it (often manually), evaluate the language against firm and regulatory rules, and document their findings. That process has three compounding problems.
- It’s slow. Reviewers spend hours on tasks that are largely mechanical — transcription, keyword scanning, rule matching — leaving little capacity for genuine judgment calls.
- It’s inconsistent. Two reviewers applying the same rules to the same video can reach different conclusions. Standards drift. Edge cases accumulate.
- It doesn’t scale. As content volumes grow, headcount requirements grow with them. There’s no natural efficiency gain from doing more of the same manual work.
For BerryPie’s prospective customers, the cost shows up in multiple ways: inflated compliance team budgets, delayed content approvals, regulatory risk from missed violations, and the kind of documentation gaps that make audits painful.
BerryPie’s founders had seen this problem up close. They knew the opportunity: automate the mechanical parts of the review process using generative AI, surface risks clearly, and give compliance teams the tools to do their actual jobs rather than spend their days transcribing videos.
The Core Question
Could an AI system watch an investment video, understand the financial language in context, check it against evolving compliance rules, and return a clear, defensible score — fast enough to fit into real compliance workflows?
The answer had to be yes. But getting there required solving several hard problems in parallel: accurate speech recognition on financial terminology, context-aware policy reasoning, a clean reviewer interface, and a cloud backend that could handle concurrent video workloads without degrading under load. All of it, in 40 days.
The Solution
A Generative AI Compliance Engine, Built for Real Workflows Aegasis Labs designed and built BerryPie’s compliance MVP end-to-end: AI engine, backend infrastructure, web application, and cloud deployment. The architecture was shaped by three non-negotiables from the start — accuracy, transparency, and security.
Accuracy, because a compliance tool that flags the wrong things (or misses the right ones) is worse than useless. Transparency, because reviewers need to understand and defend every finding, not just accept a score. Security, because the content being analyzed is sensitive financial material subject to regulatory and data protection requirements.
How It Works The platform follows a straightforward flow that mirrors how compliance teams already think about their work — but removes the manual steps that slow everything down.
- Upload. A user uploads an investment video through the web interface.
- Transcribe. The AI engine transcribes the speech, processing financial terminology accurately and preserving timestamps throughout.
- Analyze. The generative AI layer analyzes the transcript against configured compliance rules and risk categories, reasoning over context rather than simple keyword matching.
- Score. The platform returns a compliance score, specific flags with rationale, and highlighted excerpts tied to timestamps — immediately, as processing completes.
Review & Report. Reviewers manage their queue, add notes, resolve or escalate flags, and export audit-ready reports directly from the dashboard.
What Was Built
- AI Compliance Engine – LLM-based reasoning layer that checks transcribed video content against compliance rules and risk categories, returning flags with rationale and evidence — not just verdicts.
- Automatic Transcription – Speech-to-text pipeline tuned for investment and financial language, with timestamp preservation for precise evidence linking.
- Compliance Scoring & Dashboards – Per-video compliance scores with trend views and reviewer throughput analytics, giving compliance managers visibility across the entire review queue.
- Reviewer Workflow – Status tracking from ‘In Review’ to ‘Approved’, inline notes, flag resolution, and change requests — built for how compliance teams actually operate.
- Audit Trails & Reporting – Immutable activity logs, downloadable summaries, rule-level findings, and full decision trails ready for regulatory review.
- Role-Based Access Control – Separate permissions for content creators, compliance reviewers, and administrators, with secure storage of all media and results.
- Scalable Cloud Backend – Containerized services with autoscaling and object storage, designed to handle growing video volumes and concurrent reviews without degradation.
- Performance Monitoring – Real-time analytics on review SLAs, common violation patterns, and processing throughput — built in from day one, not added as an afterthought.
Technologies The platform was built on a modern, production-grade stack selected for reliability at scale:
- Generative AI (LLM-based policy reasoning and compliance analysis)
- Speech-to-Text and video processing pipeline
- Secure backend APIs and scalable data storage
- Responsive, accessible web frontend
- Cloud deployment with CI/CD, autoscaling, and observability tooling
- Real-time analytics and compliance dashboards
Every component went through user testing, performance tuning, and security hardening before go-live. The 40-day commitment included all of it.
How We Worked Together
Aegasis Labs follows a four-stage delivery model — Discover, Design, Build, Scale — that kept the BerryPie engagement structured without slowing it down. With a 40-day deadline, there was no room for ambiguity or late-stage rework.
- Discover. We started by working directly with BerryPie’s compliance subject matter experts to map real regulatory policies to machine-checkable rules. That grounding shaped everything downstream.
- Design. Alongside the compliance mapping, we designed the user flows for upload and review — making sure the interface matched how compliance teams actually work, not how engineers assume they work.
- Build. Our AI-first engineering team built the full platform: transcription pipeline, AI reasoning layer, reviewer UI, backend APIs, and cloud infrastructure. Simultaneously, not sequentially.
- Scale. Before go-live, the platform went through rigorous user testing, performance optimization, and security hardening. The launch was clean.
The Results
A Working Product. A Real Foundation. BerryPie launched their MVP in production within 40 days of kickoff. That’s not a prototype or a technical demo — it’s a live platform handling real investment videos end-to-end, from upload through AI analysis to a scored, audit-ready report. What Shipped in 40 Days
A production-ready compliance platform with an AI review engine, scalable cloud backend, reviewer workflow, and audit trail — tested, optimized, and live
Here’s what the platform delivered at launch:
- Working MVP in production, processing real investment videos across the full review pipeline with no manual transcription required.
- Accurate AI analysis using a generative AI engine that understands financial language in context — reducing the mechanical review burden on compliance teams.
- Real-time compliance scoring so reviewers see risks and flags as videos are processed, with clear rationale and timestamped evidence for every finding.
- Audit-ready records including transcripts, detected claims, evidence snippets, and a complete, immutable decision trail for every reviewed video.
- Secure and scalable infrastructure, containerized and cloud-deployed, ready to support growing content volumes and user numbers without re-architecture.
- Positive user reception, with teams reporting that the speed and clarity of the AI output helped standardize review quality across products and campaigns.
- Foundation for future capability — multi-language review, policy versioning, and deeper analytics are all extensions of the architecture already in place.
Build Your AI Product with Aegasis Labs
BerryPie came to us with a vision, a deadline, and a domain that required genuine precision.
We brought the AI architecture, engineering depth, and delivery discipline to make it real. Whether you’re a startup validating a product idea or an enterprise automating a complex workflow, Aegasis Labs works with you from strategy through deployment — as a partner, not just a vendor.
Ready to Build? If you have an AI product idea or a workflow that needs intelligent automation, we’d like to hear about it. Contact here aegasislabs.com/contact to start the conversation.