AI wherever people alone fall short
From reviewing and reconciling to reporting and forecasting — completed step by step until it is usable in real operations, not just shown in a demo.
delivered across
largest KB
reconciliation
onboarding
Most enterprise AI stops at the demo.
Plenty of companies adopt AI. The demo is impressive, the deck looks sharp. Then a few months later, on the ground, nobody uses the output for real work. It was deployed, and it sits unused.
The reason is clear: what comes out looks plausible but has no real use on the ground. If you cannot check how far the features and the numbers can be trusted, the point of automation is gone.
So the question we start from is narrow: can a person confirm why that AI produced what it did? The higher the stakes, the more that question decides whether adoption succeeds.
If you cannot trace why a result came out, we do not ship it.
We verify as thoroughly as if we were deploying it for our own use. Everything we build carries a way to check why it produced the result it did. Take the hardest case — a financial-regulation knowledge assistant: natural-language search is embedded into vectors, while tables and figures are held as structured fields. Before deployment every value is checked against the source document, and if it does not meet the guideline, that knowledge base does not reach production.
The highest-risk surface — numbers inside tables — is controlled before deployment rather than at runtime.
Your documents stay yours, and stay separated.
Auditable infrastructure built for teams that answer to regulators. Every client runs as an isolated tenant — no shared index, no shared credential.
Independent isolation
Strict separation of data, encryption keys and query logs per client workspace.
PII masking
Personally identifying data is redacted before it is written anywhere.
Prompt-injection defense
Answers stay bound to retrieved passages, so an instruction hidden in an uploaded file cannot redirect the assistant.
Deploy in your cloud
We run Anthropic Claude and Google Vertex AI, and deploy into your account and region when residency requires it.
Instant deletion
Everything you send is wiped on request, without conditions.
Verifiable by construction.
From the AX assessment your business needs, through to build.
Every step verified, held to enterprise-grade quality.
Repetitive manual work, moved to AI
Invoice reconciliation, weekly reports, document sorting and intake, approval flows — the repetitive work that eats hours every time, moved into an AI workflow. People on judgment, machines on repetition. Every result traces back to why it came out that way, so it goes into real operations, not just a demo.
AI that answers
From an internal assistant that knows your policies and manuals to customer-support and website chatbots. Every answer cites its source, and never invents one when the evidence is missing.
Writing, moved to AI
Blog and SEO copy, marketing text, report drafts. A generation pipeline that holds to your brand voice and checks its facts, producing a draft ready for a human to finish.
Forecasting and scoring
Demand forecasting, recommendation, anomaly detection, scoring. Backed by data science across finance, semiconductors and logistics — with validation designed to distrust a good-looking result.
Where AI belongs
We map the work still done by hand and rank the candidates that can realistically land in production, by priority and expected return. What to build is the next decision.
The first two weeks produce a working system, not a specification.
We do not take a deposit and start writing requirements documents. We build on your material first, show you the result, and let you decide whether to continue.
Intake
We review the feature spec together and produce the essential feature definition.
Structure & index
We evaluate the best way to implement the solution.
Verification gate
We test the model until it passes our own verification engine.
Demo & review sheet
You get a prototype you can check, plus a test sheet.
Not a uniform template, but custom solutions
built for each client's industry and operations.
Non-QM DSCR lender, North America
Underwriters were cross-checking two separate investor guidelines by hand on every file. Because the same question resolves differently depending on which investor the loan is sold to, every passage carries its investor and program label so the assistant can say "permitted under one, not the other."
- Implementation scopeGuideline collection and chunking · Institution- and product-level rule comparison · Evidence-grounded Q&A
- Validation & application1,355 knowledge chunks · 6 comparison matrices · Zero discrepancies in full-text source verification
Virtual human · real-time generative AI
A generative-AI system that holds a virtual person's identity while extending it into images and real-time video. We took virtual-human generation and real-time face-swap research all the way through to a deployed service.
- Implementation scopeVirtual human generation · Identity-preserving image expansion · Real-time face swapping
- Validation & application34 Korean patent applications · 2 PCT applications · 2 granted patents
Dispatch & route optimization
An operations-optimization engine that weighs live requests, vehicle constraints and workload balance together to find better dispatch and routing. Map data and speed prediction are joined to support on-the-ground decisions.
- Implementation scopeDynamic dispatch · Multi-vehicle route optimization · Map matching · Road-speed prediction
- Validation & application6 granted patents related to maps and routing · Vehicle-count and operating-cost optimization research
Fab process quality intelligence
A production solution that catches anomaly signals early in complex process data and analyzes the factors driving quality, with a visualization layer so engineers can inspect the findings themselves.
- Implementation scopeMultivariate anomaly detection · Quality prediction · Defect-cause analysis · Visualization tools
- Validation & applicationAdopted as an internal quality validation method · Identification of key process-control variables
AI research platform
An AI research service that gathers financial data, news and research, analyzes it, and answers questions on top. Our own quant logic is combined with generative AI to explore complex market information fast.
- Implementation scopeFinancial data collection · Proprietary analytics models · AI-powered Q&A · Web UI
- Validation & applicationIn-house R&D service in operation · Deployed on Google Cloud Platform
Content & advertising performance intelligence
An operational data solution that integrates YouTube, Google Ads and social media data to assess channel safety, analyze content performance, forecast views and subscriber growth, and measure advertising efficiency. We connected the full workflow — from data collection and scoring models to predictive analytics and dashboards.
- Implementation scopeMulti-channel data collection · Channel safety scoring · Content performance forecasting · Analytics dashboard
- Validation & applicationChannel safety assessment · Views and subscriber growth forecasting · Advertising spend efficiency analysis
Personalized recommendation system
A data product that combines item attributes, user taste and purchase/rating history to serve per-customer recommendations, with an A/B testing loop to validate and improve the experience.
- Implementation scopeUser feature engineering · Collaborative filtering and SVD · Segment-specific personalized recommendations
- Validation & applicationA/B test-driven improvement · Patent application for the recommendation algorithm
AI Sommelier
A conversational AI that recommends wine by taste, price, food and occasion and answers from a knowledge base — with screen experiences shaped to the intent of each question: recommend, pair, compare.
- Implementation scopeWine database · Query intent classification · RAG · Query-specific user interfaces
- Validation & applicationSeed database of 100 wines · 9 query categories · MVP user-flow validation
Case-law assistant for attorneys
Built on the Korean government's statutory and case-law open API as the sole source of record, with case numbers returned alongside every answer. A fabricated citation is malpractice, so refusing to answer without evidence was the entire requirement.
- Implementation scopeIntegration with the Korean National Law Information API · Hybrid vector and keyword search · Evidence-grounded answers
- Validation & applicationCase-number and source-text citations · Architecture designed to prevent unsupported answers
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Get in touch
Client names, contract terms and operating figures stay anonymized until disclosure is approved.
Small, so the people who build it are the people you talk to.
A Seoul-based team spanning business development, marketing, mathematics and computer science. No account manager takes notes and hands them off — the person who builds it listens from the start.
Financial-product planning and development PM, quant algorithms, RAG assistants — building and operating practical solutions end to end.
A technical lead who has solved problems across finance, semiconductors, logistics, recommender systems, generative AI and quantum computing with data and algorithms. He sets the bar for what we mean by "validated."
Built high-throughput data core engines (C++) at a global enterprise software company; as a startup CTO, designed service architecture on Next.js and Nest.js, led the engineering org, built payment systems, and owned security and infrastructure.
Speed comes from what is already built, not from headcount. We own our own multi-tenant optimization engine and pipeline, so we can adapt flexibly to most of a new client's onboarding.
Judge us in two weeks.
Judge us by the prototype.
- DurationTwo weeks
- Scope20-30 pages (for chatbot or library work)
- We ask forOne review call once delivered
Prefer email — info@snailsecret.kr