Conversational AI
Assistants, QA systems, and CRM-connected workflows
Representative builds across support, knowledge access, and customer communication.
ChatbotsWhatsApp
Work and case studies
Representative build themes grounded in real delivery experience.
These case studies are anonymized and intentionally avoid client names, inflated claims, or made-up ROI numbers. They are designed to show breadth across AI systems, software engineering, speech workflows, automation, and data infrastructure.
The goal is to give prospects a credible view of what the studio can build, while respecting confidential implementation details.
AI Systems
Knowledge assistant for document-heavy teams
Representative of RAG-powered question answering work built to make internal or public-facing knowledge easier to access through grounded retrieval.
RAG
Prompt engineering
Evaluation
Backend systems
UX flow
Challenge
Important information existed across multiple sources and formats, making fast access difficult for end users and support teams.
What was built
Designed ingestion, retrieval, answer orchestration, evaluation thinking, and user-facing flows for a question-answering experience grounded in curated sources.
Typical deliverables
- Knowledge ingestion workflow
- Retrieval and answer design
- Prompt and evaluation structure
- Python backend and delivery interface
Scope note: Based on real RAG system experience; client-specific details are intentionally withheld.
Applied AI and Automation
CRM-connected WhatsApp assistant
A conversational workflow integrating business messaging, OpenAI-powered logic, retrieval, and CRM-connected handling for lead or support interactions.
Chatbots
OpenAI APIs
RAG
CRM integration
Automation
Challenge
The business needed a more responsive conversational layer while keeping activity connected to internal processes and contact records.
What was built
Implemented a chatbot flow combining conversational logic, data retrieval, CRM integration, and channel-aware handling via WhatsApp and Twilio-style workflows.
Typical deliverables
- Conversation logic and fallback handling
- CRM-aware data exchange
- Lead or support workflow integration
- Deployment-ready backend structure
Scope note: Representative of real CRM-integrated chatbot system work.
Speech AI
Speech transcription and diarization workflow
Speech-oriented system work spanning transcription, speaker diarization, and usable output formatting for operational or research contexts.
ASR
Diarization
Speech workflows
Python pipelines
Challenge
Audio-heavy workflows require more than raw transcripts; they need usable structure, speaker clarity, and dependable processing pipelines.
What was built
Designed an end-to-end pipeline for audio preprocessing, ASR, diarization, output cleanup, and downstream usability for teams working with recorded speech.
Typical deliverables
- Speech processing pipeline
- Structured transcript outputs
- Speaker-aware formatting
- Operational integration points
Scope note: Grounded in real speech AI delivery experience.
Model Adaptation
LLM adaptation and evaluation workflow
Representative of model evaluation, instruction tuning, continual pretraining, and benchmarking work used to understand model behavior and quality.
Instruction tuning
Continual pretraining
Model evaluation
Benchmarking
Challenge
Generic models often need stronger adaptation and more rigorous evaluation before they can be trusted for specialized tasks.
What was built
Worked on data preparation, tuning logic, evaluation design, and benchmark-oriented analysis for adapted model behavior and performance review.
Typical deliverables
- Evaluation criteria and benchmark framing
- Adaptation workflow support
- Task-aware testing structure
- Technical reporting inputs
Scope note: Reflects real research-backed LLM work without disclosing confidential project specifics.
Automation and Data
Web scraping and data infrastructure layer
A data collection and processing setup built to support recurring business or research needs through cleaner acquisition and structured outputs.
Web scraping
Data pipelines
Automation
Monitoring
Challenge
Valuable information was spread across external sources and manual collection introduced delay, inconsistency, and reporting friction.
What was built
Developed scraping logic, cleanup steps, data storage patterns, and delivery pipelines that made collected data more usable for downstream systems.
Typical deliverables
- Scraper or collector workflows
- Transformation logic
- Storage and scheduling patterns
- Monitoring-aware pipeline structure
Scope note: Representative of real data engineering and automation themes.
Software Products
Custom portal and backend system delivery
Product-oriented engineering work focused on the application layer around user journeys, data handling, dashboards, and business logic.
Django
REST APIs
SQL
Dashboards
Product engineering
Challenge
Businesses often need operational software that fits their workflow instead of trying to force teams into generic off-the-shelf tools.
What was built
Built backend services, admin flows, data structures, and web interfaces shaped around product requirements and day-to-day operational usage.
Typical deliverables
- Application architecture
- Django or Python backend systems
- User-facing workflows and admin views
- Launch-ready product features
Scope note: Represents software delivery work beyond pure AI engagements.
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