Softwarethat Thinks.
AI systems engineered for the real world.
We build production-grade software using computer vision, AI, and modern engineering.
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 excellent at processing what has already been structured for it. The real world isn’t.
Information lives in invoices, contracts, images, conversations, transactions, systems, and workflows. Understanding it requires 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 — enabling software to 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 one pipeline. Most projects need more than one of them, which is why we build all four rather than handing you off.
- 01
See
Computer Vision
Systems that read images and video the way an operator would — locating what matters in a frame, tracking it over time, and turning it into a number someone can act on.
- Python
- OpenCV
- PyTorch
- YOLO
- 02
Understand
Document Intelligence
Invoices, bank statements and utility bills arrive in every layout imaginable. We build extraction pipelines with field-level validation and confidence scoring, so people review the edge cases instead of every page.
- Google Document AI
- AWS Textract
- Tesseract
- Custom OCR
- 03
Think
AI, LLMs & Agents
Language models are only useful when they are grounded in your data and constrained by your rules. We build retrieval, agents and tool integrations that hold up 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, APIs, mobile apps and infrastructure that put the output 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
Understand the business problem, the users, the data you already have, and the constraints that will actually shape the build.
02
Architect
Design the product and the technical architecture together, so the model, the data flow and the interface are decided as one system.
03
Build
Develop the software, AI systems, APIs and infrastructure in short cycles, with working demos rather than status reports.
04
Deploy
Move the system into production properly — monitoring, error handling, documentation and the handover that makes it yours.
05
Evolve
Watch how it behaves on real data, retrain and tune where accuracy drifts, and scale what works.
Technical depth
The model is one layer of six.
Most AI projects fail below the waterline — in the integration, the error handling, the thing that has to run unattended on a Sunday. This is the stack we build and operate.
- 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 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 works on your test set is the easy half. We build for the input that arrives malformed at 2am, and we own the deployment, not just the notebook.
- Accuracy is a number, not an adjective
- Every extraction pipeline we ship reports confidence per field. You get to see where the system is unsure and decide what a human should check, rather than trusting a single headline percentage.
- Engineering first
- The AI is one component inside a system that also needs APIs, a database, auth, logging and someone to call when it breaks. We build all of it, which is why it integrates with what you already run.
- Built to evolve
- Data drifts and requirements move. We architect so the model can be retrained and swapped without 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 around them. We take on 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 figure out what's possible — and say so if we're not the right people for it.