AI beyond implementation. Engineered for lasting business value.

AI Strategy and Readiness is a short engagement, usually 2 to 4 weeks, for teams that have several AI ideas and need them scored before any engineering starts. It covers use-case discovery, a data and infrastructure readiness check, a build-versus-buy analysis, an ROI model, and compliance guardrails.

AI-Native Web and SaaS Apps is a full build, usually 8 to 14 weeks, for a new product where AI is the core of what it does, not a feature added later. Architecture, data model, and interface are designed around the model from day one, including prompt design, cost-aware model choice, and launch support.

AI-Native Mobile App Development is a full build, usually 10 to 16 weeks, for a new iOS or Android app. On-device inference is used where latency or privacy needs it, and cloud inference where it does not, with store-ready release work and profiling for battery, latency, and cost.

LLM and Agent Applications is a full build, usually 6 to 10 weeks, for copilots, chat products, and multi-agent workflows. It includes prompt architecture, tool-use pipelines, retrieval-augmented generation, an evaluation harness tied to business metrics, and production monitoring.

MCP and Agent Tooling Development is a full build, usually 4 to 8 weeks, that gives a model safe, structured access to your database, internal APIs, file storage, and third-party services. It covers Model Context Protocol servers, tool schemas, permission scoping, and tests of whether the tools behave reliably.

Custom Model Development is a full build, usually 6 to 12 weeks, when a general-purpose model is not accurate enough for the domain. It covers fine-tuning on your data, an evaluation harness, model selection against cost, versioned prompts, and monitoring after launch.

AI Integration Into Existing Products is an incremental engagement, usually 3 to 7 weeks, that adds AI to a web app or SaaS product you already ship. It works inside the current codebase, auth model, and release process, with a feature-flagged rollout and a cost review after launch.

MLOps and AI Infrastructure is an ongoing engagement, usually starting at 4 to 8 weeks, for deploying models, watching latency and drift, and keeping token cost under control. It includes deployment pipelines, vector database tuning, alerting, and infrastructure as code.

Computer Vision and NLP is a full build, usually 6 to 10 weeks, for documents, images, and text classification. It covers extraction pipelines, object detection, intent recognition, OCR, and tuning for your language and domain, evaluated on your own data.

Workflow and Company-Wide Automation is a short engagement, usually 2 to 5 weeks, for internal operations. It starts with the process that actually wastes time, then connects AI-assisted tools to the ticketing, CRM, or internal systems you already use, with a human review step where one is needed.

Data Engineering and Preparation is foundational work, usually 4 to 8 weeks, that makes retrieval and fine-tuning reliable. It covers pipelines, cleaning and labeling, warehouse design, schemas for retrieval, and ongoing data-quality alerts.

Voice and Conversational AI is a full build, usually 6 to 10 weeks, for phone lines and in-app voice. Streaming speech-to-text, a language model, and text-to-speech run together, with barge-in, a handoff to a person when confidence is low, and call recording for review.

AI Chatbots and Website Copilots is a full build, usually 4 to 6 weeks, for pre-sales and support. The widget answers from a curated knowledge base, stays strict on pricing and capability claims, and hands a lead to a person instead of guessing.

AI Governance and Security is an audit, usually 3 to 5 weeks, before or after launch. It tests prompt injection, tool misuse, and data exposure, then delivers a severity-ranked findings report, remediation support, and a retest after fixes.