Softwarethat Thinks.
AI systems engineered for the real world.
We build production-grade software with computer vision, document AI, LLMs and the engineering that keeps them running.
Trusted by teams building in production
40+
Projects delivered
5+
Years building software
2M+
Data points processed
99.9%
Document extraction accuracy
The problem
Most software follows instructions.
We build software that understands them.
Software is brilliant at processing anything that has already been structured for it. The real world rarely is.
Your information lives in invoices, contracts, images, camera feeds, conversations, transactions and workflows. Making sense of it takes context, reasoning and the ability to connect what is being seen with what needs to happen next.
ByteWeave builds intelligent systems that turn real-world information into usable intelligence, so software can see, understand, reason, and act.
{"invoice_no": "INV-2043","date": "2026-03-14","vendor": "Northwind Trading Co.","line_items": [{"description": "Cable assembly","qty": 4,"amount": 18400.00,},… 5 more],"total_due": 64820.00, // confidence 0.71 → review"currency": "INR",}From a document on a desk to a decision inside an application, we build the technology that connects the two.
Capabilities
See, understand, think, act.
Four stages of a single pipeline. Most projects need more than one of them, which is why we build all four in-house instead of handing you off to somebody else.
- 01
See
Computer Vision
Computer vision systems that watch images and video the way a good operator would. Object detection, tracking and real-time video analytics turn a camera feed into a number your team can act on.
- Python
- OpenCV
- PyTorch
- YOLO
- 02
Understand
Document Intelligence
Invoices, bank statements and utility bills arrive in every layout imaginable. We build intelligent document processing pipelines with OCR, field-level validation and confidence scoring, so your team reviews the edge cases instead of every page.
- Google Document AI
- AWS Textract
- Tesseract
- Custom OCR
- 03
Think
AI, LLMs & Agents
Large language models are only useful when they are grounded in your data and fenced in by your rules. We build RAG pipelines, AI agents and MCP tool integrations that hold up well outside a demo.
- OpenAI
- Claude
- RAG
- Vector search
- 04
Act
Applications & Automation
The model is the middle of the job, not the end of it. We ship the dashboards, REST APIs, mobile apps and cloud infrastructure that put the result in front of the people who need it.
- React
- Next.js
- Node.js
- Flutter
- PostgreSQL
- AWS
Selected work
Systems running in production.
The ByteWeave method
How a project actually runs.
01
Discover
We get clear on the business problem, the users, the data you already hold and the constraints that will really shape the build.
02
Architect
We design the product and the technical architecture together, so the model, the data pipeline and the interface get decided as one system.
03
Build
We build the software, machine learning models, APIs and infrastructure in short sprints, and every cycle ends in a working demo rather than a status report.
04
Deploy
We move the system into production properly, with monitoring, error handling, documentation and the kind of handover that makes it genuinely yours.
05
Evolve
We watch how it behaves on live data, retrain and tune wherever accuracy drifts, and scale up the parts that are working.
Technical depth
The model is one layer of six.
Most AI projects fail below the waterline: in the integration, the error handling, the deployment that has to run unattended on a Sunday. This is the full stack we build and operate, from the data layer up to the infrastructure.
- 01
Application
What people actually use
- React
- Next.js
- Flutter
- 02
APIs
How it reaches the software you already run
- REST
- Webhooks
- Auth
- Rate limiting
- 03
Business logic
Validation, thresholds, human review routing
- Python
- Node.js
- Queues
- Schedulers
- 04
Vision / AI / LLMthe model
Where unstructured input becomes meaning
- PyTorch
- OpenCV
- OpenAI
- Claude
- 05
Data
Documents, images, video, transactions
- PostgreSQL
- MongoDB
- S3
- 06
Infrastructure
Deployment, monitoring, scale
- AWS
- Docker
- CI/CD
- Observability
Five of the six have nothing to do with machine learning. They are the integration, the error handling and the day-to-day operations, and they are where AI projects actually fail.
Why ByteWeave
AI is easy to demo. Engineering it is harder.
- Production over prototypes
- A model that scores well on your test set is the easy half. We build for the input that arrives malformed at 2am, and we own the deployment and the monitoring around it, not just the notebook.
- Accuracy is a number, not an adjective
- Every data extraction pipeline we ship reports a confidence score for each field. You get to see exactly where the system is unsure and decide what a person should check, instead of trusting one headline percentage.
- Engineering first
- The AI is one component in a system that also needs APIs, a database, authentication, logging and someone to call when it breaks. We build all of it, which is why it integrates cleanly with the stack you already run.
- Built to evolve
- Data drifts and requirements move. We architect the system so a model can be retrained, versioned and swapped out without anyone rewriting the application around it.
About
Small team.
Serious engineering.
ByteWeave is a small studio in Bangalore, working with teams wherever they are. The people who scope your project are the people who build it. There is no account layer between you and the engineering.
We have spent five years shipping document understanding pipelines, video analytics, LLM tooling and the web and mobile software that wraps around them. We take on the work where the hard part is making something reliable, not making something impressive.
- Based in
- Bangalore, India
- Working with
- Startups & SMBs
Have a problem
worth solving?
Tell us what you're building. We'll help you work out what's actually possible, and say so if we're not the right people for it.