Engineering at a
higher velocity.
Senior engineering teams paired with AI-native workflows. Production-grade systems, delivered fast and built to hold.
We don't start with code.
We start with the problem.
Understand first, build fast, take ownership. That's the whole approach.
Understand before we build
We spend the first days learning your domain, not pitching solutions. A clear problem definition means fewer rewrites, tighter scope, and software that solves the right thing.
Senior engineers on every project
No hand-offs to junior teams after the sales call. The people who scope your project are the people who ship it, and you hear from them before you have to ask.
AI as a multiplier, not a shortcut
We use AI tooling to move faster on the repeatable parts of engineering, so our people spend their time on architecture, edge cases, and quality. Speed without the technical debt.
Everything you need to
ship production-grade.
From zero to one, and one to scale. We provide the senior engineering and AI-native velocity to make it happen.
AI Solutions & Automation
Custom AI workflows, process automation, LLM integrations, and intelligent business systems.
Explore capabilities
AI Agents & Copilots
Production-ready agents for support, sales, operations, research, and internal workflows.
Explore capabilities
Machine Learning & Predictive Systems
Forecasting, recommendation systems, classification, computer vision, NLP, and custom ML models.
Explore capabilities
Data Engineering & Intelligence
Data pipelines, warehouses, analytics platforms, RAG systems, vector search, and AI-ready data infrastructure.
Explore capabilities
Application Development
Full-stack web and mobile products with AI deeply integrated into the product experience.
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Higher velocity,
by design.
Once the problem is clear, we build. A working version first, real feedback second, long-term design third. It's faster because every big decision is made on evidence, not on a spec written before anyone used the thing.
Understand
Enough discovery to define the problem, the users, the constraints, and what success looks like. We stop before it turns into a research project. Anything we don't know yet, the first build will tell us faster than another workshop would.
Understand
Enough discovery to define the problem, the users, the constraints, and what success looks like. We stop before it turns into a research project. Anything we don't know yet, the first build will tell us faster than another workshop would.
Build a working version
A basic, workable MVP. Not a prototype, not a deck: real code, deployed, and usable by real people. We keep the architecture deliberately simple and reversible, so nothing we build now blocks a better decision later.
Learn from real use
We put it in front of users or your team, measure what happens, and collect feedback early and often. This is where assumptions get replaced by findings, and where we discover which problems actually matter.
Design for the long term
Now with evidence, we design the durable version: architecture, data model, integrations, and roadmap. Decisions that would have been guesses in week one are grounded in what we've seen. This is where the MVP becomes the v1, not the rewrite.
Scale & support
Continuous delivery against the roadmap, with monitoring, performance work, and support as usage grows. Stay with us as an extension of your team, or take full ownership with our help.
The kind of projects
we have worked on.
From offline-first applications to autonomous AI agents, we build software that solves complex business problems. Here is a selection of systems we've designed and shipped.
Law Baje
Search legal documents in natural language and retrieve the exact clauses and sub-clauses relevant to a query.
Read the case →- Lets users describe legal questions in natural language instead of manually navigating large legal documents.
- Retrieves exact clauses and sub-clauses rather than producing unsupported free-form answers.
- Combines document retrieval with Natural Language to SQL for querying structured legal information.
- Uses retrieval-grounded generation so responses remain tied to the underlying legal source material.
- Designed to reduce the time required to locate relevant provisions while preserving source traceability.
Research Agent
An autonomous research system that forms hypotheses, investigates them in parallel, and preserves evidence so previous work can be reused.
Read the case →- Shortlists a set of plausible hypotheses for a research question before beginning deeper investigation.
- Runs parallel sub-agents that independently gather evidence and attempt to accept or reject each hypothesis.
- Stores useful findings and experiment results so later runs can reuse previous work instead of repeating the same investigation.
- Maintains provenance across each run, connecting conclusions to supporting evidence, executed steps, code, metrics, and intermediate results.
- Uses execution logs and evaluation feedback to identify failure patterns and improve subsequent agent behavior.
Document Intelligence OCR
Turns documents into validated structured data and automatically triggers downstream business workflows.
Read the case →- Self-hosted so sensitive enterprise documents can be processed without sending data to third-party document-processing services.
- Runs OCR and extracts required information into a strict predefined schema instead of returning unstructured text.
- Applies field-level validation, normalization and business rules after extraction before the data reaches downstream systems.
- Uses extracted fields to make workflow decisions, such as determining how a document should be processed or whether manual review is required.
- Connects to n8n workflows for operational actions such as entering invoice information into Zoho Books, with human review where correctness must be guaranteed.
Customer Support Chatbot
An agentic customer-support assistant grounded in company-specific information and validated against a dedicated evaluation suite.
Read the case →- Grounds responses in company-specific knowledge rather than relying solely on the model's pretrained knowledge.
- Uses retrieval to identify relevant information before generating an answer, keeping responses connected to available source material.
- Supports agentic workflows where requests can be routed through different retrieval or reasoning paths depending on the question.
- Includes evaluation datasets and automated graders to repeatedly test answer quality, retrieval performance and failure cases.
- Adds telemetry and execution traces so incorrect responses can be inspected, reproduced and improved instead of treated as isolated chatbot failures.
Liftrava
An offline-first fitness PWA that lets lifters log every set with zero signal and builds their training plan from what they actually train.
Read the case →- Full-stack system spanning four deployed applications, verified through end-to-end testing across offline and slow-network scenarios.
- Works completely offline with an idempotent synchronization process and local replay queue to reliably sync sets upon reconnection.
- Automatically builds adaptive training plans and generates next-session progression targets from the athlete's actual training history.
- Features a 187-exercise library, 3D muscle-recovery mapping, PR tracking, guest logging, and integrated Paddle subscriptions.
Things people ask before
they work with us.
Let's build something
that works.
Tell us where you're trying to go. One of the founders replies within 48 hours with an honest view on how we can help.