AI
Production AI systems, not demo theater
We design, build, and run applied AI inside real products: copilots, retrieval, agents, and automation with evaluation, guardrails, and cost controls from day one.
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Live AI products
Real estate marketing, speech assessment, and workflow platforms in production
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Delivery shapes
Greenfield AI products, RAG pipelines, copilots, and reliability hardening
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Model routing
OpenAI, Anthropic, and open models chosen by task, latency, and cost
Eval-first
Release discipline
Golden datasets and regression checks before prompts or models reach users
Capabilities
What we build with AI
Applied systems inside your product stack, scoped to measurable outcomes and safe failure modes.
Product
Copilots and in-product assistants
Context-aware helpers embedded in workflows your team already uses, with logging and fallbacks when the model is uncertain.
RAG
Retrieval and knowledge search (RAG)
Document Q&A, internal search, and support deflection grounded in your data with clear citation and access boundaries.
Agents
Agents and workflow automation
Multi-step tasks with tool use, approvals, and human-in-the-loop checkpoints for high-risk actions.
Pipelines
Classification and extraction pipelines
Structured outputs from unstructured inputs: tagging, summarization, entity extraction, and routing rules.
Reliability
Evaluation and monitoring
Golden datasets, automated regression suites, cost dashboards, and rollout flags for model and prompt changes.
Audit
AI reliability audits
Review of an existing AI feature for hallucination risk, latency, cost drift, and missing guardrails before you scale traffic.
Principles
What production AI requires
Non-negotiables we apply before any model reaches real users.
Narrow scope with clear metrics
One workflow, one success measure, and explicit refusal cases before prompt tuning begins.
Evaluation before optimization
Golden datasets and automated checks gate every prompt or model change.
Guardrails at the boundary
Schema validation, moderation hooks, rate limits, and redaction before data crosses the model API.
Operate like any other feature
Owners, dashboards, rollback paths, and review cadence when vendors or models change.
AI in production
Products with applied AI
Selected client work where AI is part of the live product, not a slide-deck prototype.
Industries
AI by vertical
Focused pages for common AI engagement shapes. Each links to related production work where available.
EdTech
View pageAI for language and learning products
Education teams use AI for assessment, feedback, and analytics, not for replacing instructors. We build scoring integrations, learner-facing flows, and admin dashboards that survive real classroom load.
Real Estate
View pageAI for real estate marketing and agent workflows
Brokerages and proptech teams need marketing output faster than manual production allows. We build agent-facing tools where AI drafts assets inside approval workflows, with brand and compliance guardrails built in.
Stack
Tools we ship with
Providers and frameworks selected per use case. We avoid lock-in where routing and fallbacks keep you flexible.
Models and APIs
OpenAI · Anthropic · Ollama · Azure OpenAI
Orchestration
LangChain · Custom pipelines · Queue workers · Feature flags
Data and retrieval
Vector stores · Embeddings · PostgreSQL · Object storage
Observability
Structured logging · Token cost tracking · Eval runners · Alerting
Process
How we ship AI safely
The same rhythm we use on service engagements, tuned for model risk, data handling, and production load.
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Use case and risk mapping
Success metrics, failure modes, data handling, and compliance constraints.
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Prototype and evaluate
Small experiments with golden datasets before full integration.
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Architecture and design
APIs, queues, caching, and fallback paths planned for production load.
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AI feature development
Models, prompts, retrieval, and tools wired into your application.
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Integration and testing
Eval suites, load checks, cost monitoring, and moderation hooks.
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Deployment and launch
Feature flags, staged rollout, and logging for safe production release.
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Monitor and iterate
Regression checks and model upgrades without quality drift.
Writing
Notes on shipping AI in production
Essays on evaluation, guardrails, and operating model features like real software.
FAQ
Questions about our AI work
For company-wide process and tech stack questions, see the homepage FAQ.
Ready to ship AI that survives production?
Share your use case on the contact page. We will reply with scope, evaluation approach, and the right engagement shape.