AI that ships. Not AI that demos.
We build RAG pipelines, LLM and agent integration, voice AI, and computer vision. Then we run them in production for enterprise customers. Real data. Your security model. Live traffic.
Most AI projects look great on a laptop. Then they meet real data and fall apart.
The demo used clean sample data. Your data is messy. The demo ignored access control. Your customers expect their data to stay theirs. The demo ran once. Production runs every second, all day.
That gap is where AI projects die.
You need this capability when the demo already worked and now the hard part starts. When the model has to run on your real data. When it has to respect who can see what. When it has to stay up, stay fast, and stay accurate at scale.
We are builders, not advisors. We do not write you a strategy deck. We ship the system and we keep it running.
The hard part of AI was never the model. It is everything around the model.
We build specific systems that solve specific problems.
LLM integration
We wire large language models into your product. Prompt design, output validation, cost control, and fallback logic. The boring parts that decide whether it works in production.
RAG pipelines on your own data
Retrieval-augmented generation grounded in your documents. The model answers from your content, not from guesses. We handle ingestion, chunking, vector search, and the access rules that keep each customer's data separate.
Agentic systems
LLM agents that take steps, call tools, and orchestrate a task. We build the orchestration layer that keeps them predictable.
Voice and video AI
Real-time conversation with a virtual avatar. We built this for Allego: a live video persona a sales rep can practice against. More on that below.
Computer vision
Image models that classify, detect, and identify. We trained one to tell individual lions apart from photos. That is below too.
Custom model deployment
Your model, in production, with monitoring. We deploy it, watch it, and tune it as the data shifts.
A RAG request, end to end, with access control
Every question passes a per-tenant access filter before retrieval, so answers are grounded only in data the user is allowed to see.
Every claim points to a system we built and run.
Every claim above points to a system we built and run. Here are four.
AI sales roleplay, live for enterprise customers (Allego). We built a real-time virtual avatar that sales reps practice against. The rep talks. The AI persona talks back, on video, in real time. It is built on LiveKit for streaming, LiveAvatar for the rendered persona, and an LLM behind a Python agent orchestration layer. This runs in Allego's revenue enablement platform, used by a quarter of the Dow Jones Industrial Average companies.
Conversation intelligence pipeline (Allego). We built the pipeline that turns sales calls into coaching insight. It records and transcribes calls, normalizes the text, detects topics, and surfaces the moments that matter. Managers find the key part of any call in seconds instead of listening to the whole thing. Read the full story: 9 years of engineering partnership at Allego.
Computer vision for lion identification (Simba). The Gujarat Forest Department needed to identify individual Asiatic lions from photos. Asiatic lions have no obvious coat patterns. The signal is in their whisker spots, which stay fixed for life. We built a model on EfficientNet, fine-tuned for this one job. It reached around 90% accuracy on a small, hard dataset. We solved the data shortage with video: upload a clip, the system pulls training frames from it. An automated pipeline handles validation, augmentation, and training. Identification dropped from hours of manual work to seconds.
LLM and voice learning (ScholarNest). We engineered an AI learning platform where a child speaks an answer and the system judges if they actually understand the concept. Speech to text, then semantic comparison against the expected answer using a vector database (Weaviate) and an LLM scoring layer. Built on Flutter, Python, FastAPI, and PostgreSQL. See more in our case studies.
We use production-proven tools. No resume-driven development.
This sits on top of the rest of what we do: enterprise search at billion-document scale and analytics engineering. AI works better when the data layer underneath it is solid.
Start small. Prove it. Scale.
You do not have to bet big on day one. A fixed-fee assessment reads your data, systems, and use case. A fixed-scope pilot puts one use case into production with monitoring. Then a senior team owns the AI work and runs it for years. This is the Allego model.
- 01 Assess
Architecture & Readiness Assessment
A fixed-scope engagement. You get an architecture blueprint, risk analysis, roadmap, and ROI estimate.
- 02 Prove
Pilot / Proof-of-Value Build
One real use case, shipped to production, with monitoring in place.
- 03 Scale
Embedded Engineering Partnership
A senior team owns modules end to end, for years. The Allego and benelog model.