Ninetwothree

NineTwoThree has been building products since 2012. Its team includes PhD-level machine learning engineers, certified product managers, DevOps engineers, and full-stack engineers. The company has built 14 internal startups and 150 products that generated up to one billion dollars.

Track record

• 70+ experts
• 150+ enterprise projects delivered
• Projects delivered for 75 global clients
• 98% of projects meeting deadlines
• 90%+ team retention over the last four years
• Boston headquarters with global delivery

NineTwoThree is SOC 2 Type II and HIPAA certified. Its work spans industries including e-commerce and retail, fintech, healthcare, logistics, manufacturing, marketing, media and entertainment, non-profit, SaaS and cloud, environmental services, and IoT and smart devices.

Leadership

NineTwoThree's leadership includes CEO Andrew Amann, CTO Pavel Kirillov, COO Tanya Kovalyova, and other product, engineering, machine learning, operations, and marketing specialists.

The company also shares AI insights through stage talks, podcasts, consulting, keynotes, case studies, blogs, resources, and news.

AI foundations

• AI consulting: Audit workflows and data, rank opportunities by return on investment, and create a buildable roadmap.
• AI strategy: Define models, user experience, architecture, and success metrics before development begins.
• AI knowledge bases: Turn internal documents, tickets, and organizational knowledge into secure, grounded answers.
• Secure and compliant AI: Build privacy, access control, auditability, and enterprise compliance requirements into AI systems.

AI-native services

• AI workflow automation: Automate repetitive work with autonomous agents, business-rule workflows, data analysis agents, decision agents, and multi-agent orchestration.
• Conversational AI: Create production chat experiences grounded in organizational knowledge and connected to business systems.
• Chatbots and voicebots: Build user-facing conversational experiences for real-world flows.
• AI agent development: Create agents that query databases, update systems, complete tasks, and operate across an organization's technology stack.
• Dashboard agents: Interpret Power BI dashboards, explain changes, and answer follow-up questions with business context.
• SQL agents: Let users retrieve database insights using natural-language questions.
• Productivity agents: Draft, summarize, schedule, and automate everyday work.
• Model Context Protocol (MCP): Connect agents to business tools with relevant context.
• Machine learning: Develop predictive models and decision systems trained on the client's data.
• Computer vision: Use image and video detection, classification, and extraction to automate visual work.

Product and data engineering

• Custom web app development: Build production-grade web applications around an organization's workflows.
• Data integration and architecture: Unify fragmented systems through clean inputs, durable pipelines, and resilient integrations.
• Mobile app development, IoT development, retained teams, and maintenance and service: These are also listed among NineTwoThree's services.

1. AI discovery sprint

The process begins with conversations with NineTwoThree leaders who have built more than 150 projects. If the teams are aligned, a second call includes product managers and engineers to scope the project. The final step is a proposal presentation.

2. Rapid validation sprint

NineTwoThree immerses itself in the client's business, industry, and existing software. The team then builds a functional proof of concept to validate technical feasibility and produces an architectural blueprint and technical implementation roadmap.

3. Development and build

The team builds production-grade AI software inside the client's infrastructure, with real-time visibility into tasks. NineTwoThree's Full Transparency Development™ approach provides visibility throughout development, with testable return on investment as the goal.

4. Launch, scale, and own

After launch, NineTwoThree measures results, scales AI across the organization, and uses guardrails and key performance indicators to manage outcomes. The client's team is trained to operate and manage the delivered system, or NineTwoThree can provide ongoing maintenance and support.

Ownership and delivery principles

NineTwoThree states that clients receive full ownership of delivered code, infrastructure, and other project assets. Its services pages also state that the company builds until the client no longer needs agency dependence. The team builds with Claude Code while taking responsibility for architecture, maintenance, enterprise complexity, and production systems.

For e-commerce projects, NineTwoThree works in agile sprints with weekly demos. For fintech projects, it describes Monday.com dashboards, weekly demos, and direct access to engineers as part of its transparency approach.

Capabilities

• Recommendation engines that analyze behavior, visual preferences, and real-time session context
• Integrations with ERPs, CRMs, and third-party logistics providers
• Real-time inventory synchronization and order routing
• Custom Shopify apps and Magento modules
• Headless commerce architectures
• Cloud-native systems designed for significant traffic spikes during peak seasons
• Security practices covering SOC 2 Type II, HIPAA, and PCI-DSS requirements

NineTwoThree can connect a storefront to an ERP or warehouse management system through APIs to keep stock levels accurate across channels, prevent overselling, and automate low-stock alerts.

Reported results and experience

• 8 projects delivered
• 60 days cited as the average time to measurable return on investment
• 14+ hours saved daily through AI SKU matching, with 99% match accuracy across more than 500,000 wine records
• A supporting AI note-taking app launched in 22 weeks and created a new revenue stream

NineTwoThree states that typical custom AI e-commerce projects, such as recommendation engines or dynamic pricing tools, move from kickoff to production in 8–12 weeks, with a proof of value targeted within the first 60 days.

For e-commerce engagements, NineTwoThree states that clients own 100% of the code, custom models, and data.

Capabilities

• Risk prediction, fraud detection, and financial intelligence using machine learning
• High-frequency, low-latency systems designed to process millions of transactions
• KYC/AML, PCI-DSS, GDPR, and banking-standard considerations
• Integration with legacy banking systems and third-party APIs
• Connections to Plaid, Yodlee, Stripe, Adyen, and core banking APIs
• Decentralized applications, smart contracts, and secure crypto wallets
• Encryption at rest and in transit, OWASP Top 10 practices, and PCI-compliant environments

Reported results and experience

• More than 10 years building fintech products
• A project that increased page views by 4.6 times in four months and doubled organic traffic
• An AI chat layer that unified 15,000 news articles per day into a financial research hub
• A machine learning model that predicted house sales with 85% accuracy
• An Android application that added more than 5,000 users at launch
• Personalized investing delivered on iOS and Android

NineTwoThree states that a typical custom fintech MVP takes 3–6 months, depending on the project. The company says it transfers code repositories, documentation, and design assets to the client upon delivery.

Capabilities

• OEE, takt-time, downtime, quality, and production-target visibility
• Predictive maintenance and machine-learning systems
• Integration with PLCs from Siemens and Allen-Bradley
• SCADA and industrial sensor integrations using OPC UA and Modbus
• Edge gateways and retrofit sensors for older equipment
• Bidirectional integrations with SAP, Oracle, and Epicor
• Real-time, low-latency dashboards
• Network segmentation, encryption at rest and in transit, and role-based access controls
• Compliance support for FDA 21 CFR Part 11, AS9100, and IATF 16949, including e-signatures, audit trails, and validation documentation

NineTwoThree's manufacturing software is designed to extend rather than replace an existing ERP. It can pull work orders from an ERP and send production counts and material usage back to it.

Reported experience

• More than 10 years of manufacturing expertise
• A reported average downtime reduction of 90%
• A custom manufacturing workflow that integrated with an ERP, improved customer experience, and delivered a scalable web application

NineTwoThree states that manufacturing projects typically range from $150,000 to $500,000, depending on complexity such as the number of PLC integrations and AI modeling requirements. The company starts with discovery to establish a fixed budget and roadmap.