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The AI services we build and run in production
Bitontree embeds senior AI engineers into your sprint cadence to build agents, RAG systems, document AI, chatbots, and workflow automation, then stays to run, improve, and scale them. We bring the full software engineering alongside, so AI lands inside your real stack instead of stalling as a demo.
Trusted by global enterprises and venture-backed teams
AI we build and run in production
Eight ways we put AI to work inside your stack, built by embedded engineers, owned past launch.
The engineering that gets AI into production
AI rarely ships alone, it needs APIs, interfaces, data plumbing, and a product around it. The same teams that build our AI also bring full-stack software, web, mobile, and ERP engineering, so your AI lands inside production software instead of a notebook. Each of these stands on its own, too, when you need a build without an AI component.
Product Engineering
The product your AI lives in, custom software, web and mobile apps, and the UI/UX that makes it usable. We build the application layer so AI features ship inside a real product, not a notebook.
Systems Integration
Wiring AI into the systems you already run, ERP, CRM, data pipelines, APIs, and internal tools, so models act on real data and their output lands where the work actually happens.
Ongoing Production Support
Monitoring, maintenance, and iteration for software and AI systems alike, because production systems decay if no one owns them. The team that built it keeps it running and improving.
How we work with you
Embedded AI Engineering Team
Senior AI engineers who join your sprints, standups, and repo, not a vendor lobbing builds over the wall. We become the AI capacity you'd otherwise spend twelve months hiring for, and we stay to run what we build.
Project-Based AI Build
A scoped, fixed-outcome build when you already know what you want shipped, a chatbot, an agent, a document pipeline, a RAG system. Clear deliverable, clear timeline, production handover included.
AI Discovery & Strategy
A short, structured engagement for teams that need to figure out where AI actually pays back before committing build budget. You leave with a prioritized roadmap, a working PoC, and a costed plan to production.
Live AI systems, named outcomes
Production builds running today for healthcare, logistics, and sales teams.
How we build production AI systems
A four-phase path from "we think AI could help here" to "this system is running, monitored, and improving."
01
Step 1: Discover
Structured workshops with your ops, product, and engineering leads to pin down the highest-ROI AI use cases on your real data and workflows. You leave with a prioritized opportunity map, success metrics, and a build sequence.
Stakeholder workshops
Data & workflow audit
Use-case prioritization
Success metrics
Build sequence
02
Step 2: Design
Solution architecture, retrieval design, integration points, eval strategy, and compliance posture. This is where the production plan, not just the demo plan, gets locked.
Solution architecture
Retrieval design
Integration mapping
Eval strategy
Compliance posture
03
Step 3: Build
Embedded AI engineers build, integrate, and deploy inside your stack with CI/CD, observability, and human-in-the-loop checkpoints from sprint one. You see working software on real data every two weeks, not at the end.
Embedded engineering
CI/CD pipeline
Observability & tracing
Human-in-the-loop checkpoints
Bi-weekly working demos
04
Step 4: Scale
After launch we stay to monitor, evaluate, and improve, tightening prompts, retraining retrieval, adding use cases, and expanding to the next workflow. AI systems decay if no one owns them; we own them with you.
Continuous monitoring
Prompt & retrieval tuning
Eval-driven iteration
Use-case expansion
Shared ownership
Industries where our AI is already running
Specific systems, specific integrations, specific compliance, not horizontal demos.
Healthcare
We build HIPAA-aware AI for US clinics, digital health companies, and provider groups, clinical documentation, patient outreach, intake, and adherence systems. For scoped healthcare deployments, we support BAAs, PHI-safe pipelines, and integration patterns for Epic, Cerner, Athena, and FHIR-based stacks. Live today: a nightly medication adherence voice system and SOAP-note automation.
Logistics
AI for freight, customs, and supply-chain operators where document load and exception handling slow everything down. We build invoice and document AI, customs paperwork automation, and workflow agents that sit on top of TMS, WMS, and freight platforms. Live today inside a logistics enterprise in Singapore.
Legal
AI for law firms and legal advisory teams, matter intake, legal research, contract review, and case-brief drafting with citations and audit trails. Built for the way attorneys actually work, not generic chat over a document. Deployment running at a US legal advisory firm; public case study pending, with 50% research time saved reported by the client.
SaaS
Embedded AI for B2B SaaS products, in-app copilots, agentic workflows, and RAG over your customers' data with multi-tenant isolation and observability. We build the AI surface area your product team can't get to fast enough, then run it with you.
E-commerce
AI for Shopify, WooCommerce, and Magento merchants, product discovery, support automation, merchandising agents, and post-purchase flows that move conversion and CSAT without breaking your stack.
Manufacturing
AI for plant and operations teams, predictive maintenance, quality vision, and ops copilots wired into ERP, MES, and IoT/sensor data. Built for the realities of OT environments and brownfield systems.
What clients say about working with us
Healthcare, delivery, consulting, and founder teams who shipped with Bitontree.
Healthcare
Bitontree transformed our clinic's workflow with AI SOAP Notes. Documentation that once took hours is now instant, letting providers focus on patient care. Their expertise and commitment to quality truly stand out.
Dr. Josh Franklin
Doctor, Harmony Health Clinic
Enterprise
Yash and his team are great, and I will continue to use them for different enterprise level projects. What drew me was the way they provided suggestions outside of what I asked, which turned out to be exactly what I needed.
Martavis P.
Delivery Manager
Manufacturing
I had the pleasure of working with Bitontree on a cloud-based ERP application, and I was thoroughly impressed with their work. They demonstrated exceptional technical skills, a clear understanding of modern web development, and a proactive approach to problem-solving.
Saurav G.
Consultant
Healthcare
Bitontree transformed our clinic's workflow with AI SOAP Notes. Documentation that once took hours is now instant, letting providers focus on patient care. Their expertise and commitment to quality truly stand out.
Dr. Josh Franklin
Doctor, Harmony Health Clinic
Field notes from shipping AI
How we build, evaluate, and run production AI, written by the engineers and leads doing the work.
Frequently asked questions
Do you still do general software development, or only AI?
AI is our core, embedded teams that build and run agents, RAG, document AI, chatbots, and automation. Our software engineering exists to ship that AI into real production: APIs, web and mobile apps, ERP, and the product around it. You can also engage us for a standalone software build with no AI component; it's supporting work, not the headline.
How do engagements work, embedded team or fixed project?
Both. Most teams embed a dedicated AI engineering team into their sprints as ongoing capacity, you set the roadmap, we staff the build and keep iterating across use cases. When you already know the exact outcome you want, a fixed-scope project with defined milestones and a clean handover is the better shape. Many start project-based and move to embedded as the roadmap grows.
Which industries do you build for?
Our deepest production experience is in Healthcare, Logistics, Legal, and SaaS, where named systems are running today. We also build for E-commerce and Manufacturing teams. If your sector isn't listed but the use case is AI agents, RAG, document AI, or workflow automation, the fit assessment is the fastest way to confirm whether we're the right team.
How fast can you get something into production?
Discovery and design typically run weeks 1-4. A first production build usually lands between weeks 4 and 16, depending on integration depth, data readiness, and compliance scope. Most clients see a working system on real data within the first month and a live deployment inside the first quarter, not a year-long roadmap before anything ships.
What is the AI Fit Assessment?
A 30-45 minute working session with a senior AI engineer, not a sales rep. You walk through the workflow or system you have in mind, we pressure-test feasibility, flag the integration and data realities, and come back with an honest scope, timeline, and the shortest path to production. If we're not the right fit, we'll say so.
How do you handle security and HIPAA?
We default to PHI-safe pipelines, BAA-ready vendor stacks, audit logging, and least-privilege access from day one. For US healthcare clients we sign BAAs and align the architecture to HIPAA before any production code is written. For SaaS and enterprise clients we work to SOC 2-aware practices around access, secrets, and vendor posture so your security review stays straightforward.
Let's scope your AI build
Tell us about the workflow, system, or use case you want AI on. We'll come back with an honest read on what's buildable, what isn't, and the shortest path to production, usually within one working day.
6+
Years Of Experience
40+
Skilled Professionals
105+
Projects Delivered
35+
Global Clientele Served