AI that takes the busywork off your operations team

No more off-the-shelf AI

A solution built for everyone fixes everyone's problem but yours.

• 01

  Slow, unoptimized processes

  Too many manual steps, too much waiting. A request that should take minutes drags on for days.

• 02

  Inflexible vendors, pre-built solutions

  Ask for a change and the answer is always “can’t be done.” Their product’s already built - you’re the one who has to adapt.

• 03

  Data everywhere, useful nowhere

  Spreadsheets, inboxes, scattered data. You can feel what an organized approach could unlock - but between one fire and the next, you never get to it.

• 

  Internal platform · 2023-2026

  From one script to the platform a firm runs on

  A firm came to us with one weekly irritation: someone logged into credit-bureau portals - CRIF, then CTC - pulled reports by hand, read off the result, and copied it where it needed to go. Slow, dull, and exactly the kind of work that quietly eats an afternoon. We built a bot to do it. That was meant to be the whole job. But once that bottleneck cleared, the next one stood out, and then the one behind it, so we kept going. What started as a single automation is now the platform the firm runs its day on. It carries a client from first contact to a managed case file: the credit checks pull themselves and surface as a row of traffic-light indicators on one screen instead of five browser tabs, guided interviews turn into finished documents, notifications reach the team in-app and on their phones, and everything stays in sync with the calendar they already live in. It gives the team back somewhere around 15-20 hours a week: the portal checks, the manual drafting, the "did anyone hear back?" that used to ricochet around the office. Built on FastAPI, PostgreSQL, React, and Docker, async throughout and deliberately careful with sensitive financial data. The lesson we keep relearning: the best internal tools earn their scope by solving one real problem well, and then the next.

  ~15-20 hours/week given back to the team

  • Process automation
  • AI
  • Fintech
  • Digitalization
  • GDPR
• 

  Data & Text Mining · 2026

  Social & Text Mining Dashboard

  The client asked for a text-analysis pipeline that kicks off automatically when a file is uploaded and presents the results in a Streamlit dashboard - without writing a line of code. We built a social and text mining dashboard powered by a robust pipeline that extracts, analyzes, and visualizes insight from text. Everything runs locally, so the data never leaves the client's environment. ## What the platform does - Token analysis: distributions, anomaly detection, and statistics. - Topic modeling: BERTopic, keyword extraction, and LLM-based classification of tokens and concepts. - Emotion and entity recognition: LLM-based entity extraction. - Personas analysis: tone and sentiment analysis. - Lexical clusters and network analysis: cluster and network analysis powered by networkx. ## From charts to a decision Instead of reading every chart one by one, the client gets a final report written by a dedicated LLM backend: a ready-to-share synthesis of what the data is saying, produced in a few clicks. ## Tech stack - Pipeline and analysis: Python (BERTopic, networkx) - Dashboard: Streamlit - Report generation: LLM-powered backend - Execution: local, triggered on file upload

  From a file upload to a shareable insight report - self-serve, no code required

  • AI
  • Text Mining
  • NLP
  • Data Analytics
  • Dashboard
• 

  Machine Learning & Analytics · 2025

  Business Quote Prediction

  The client asked us for a business quote prediction system capable of self-retraining automatically every time new data is saved to the database. We built a pipeline that, from incoming features, automatically selects the best model and trains it. A human-in-the-loop system saves validated responses and reuses them as training data for the next cycle, so predictions improve over time. ## What the system does - Automatic model selection: from the features, the pipeline chooses and trains the best model. - Hyperparameter tuning and model versioning. - Human-in-the-loop: validated responses are saved and used for subsequent training. - Input parsing and error handling. - Streamlit-based interface. ## The prediction module The user is greeted by the prediction module, where they can enter data manually, upload a .CSV file, or paste it directly. The trained model generates predictions and displays them on screen, along with useful context information for interpretation. ## The monitoring module Monitoring results is central to this project. The client wanted a comprehensive dashboard showing statistics and technical details for exploration: regression metrics, cross-validation scores, residuals on train and test sets, and more. ## Tech stack - Models and pipeline: Python - Interface: Streamlit - Data: integration with the client's database - Training: recurring, triggered by new data

  A self-maintaining model that retrains on every new data point — human validation improves subsequent predictions

  • AI
  • Machine Learning
  • Predictive Modeling
  • Analytics
  • Dashboard