AI app development built around real intelligence

We build AI-powered applications with LLMs, agents, and intelligent workflows embedded where they create real value for your users.

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Trusted by hundreds of businesses

QCells
American Express
Coca-Cola
Sotheby's International Realty
Zapier
Margaritaville
Somewhere
Dataiku
medtronic
Herzig
Altriarch

When manual work drains your business

Products that win embed AI into core workflows. Bolt-on features rarely survive contact with real users.

RAG knowledge bases, autonomous agents, intelligent workflows. Production-grade AI your users depend on.

Enterprise consultancies quote six figures and six months. You need AI that works now.

What real AI product development looks like.

We approach AI development by asking what outcome the product needs to deliver, then working backward to the architecture. AI is a powerful tool that only matters if it solves real problems better than alternatives.

The difference: Most companies add AI as a feature. The products that win embed intelligence into core workflows, making decisions and automating judgment that previously required humans.

What we build with AI development.

From intelligent applications to autonomous workflows, we build AI systems that drive actual business value.

AI-Powered Apps

Full applications with AI at the core — intelligent processing, generation, or decision-making central to the product experience.

Generative AI Products

Applications powered by LLMs and image generation — content creation tools, writing assistants, and design generation systems.

AI Agents & Workflows

Systems that perceive, decide, and act — agents completing multi-step tasks and working toward goals autonomously.

RAG Systems

Retrieval-Augmented Generation for document Q&A, knowledge bases, and enterprise search grounded in proprietary data.

Chatbots & Conversational AI

Support bots, sales assistants, and onboarding guides powered by modern LLMs — not rigid decision trees.

AI-Assisted Internal Tools

Operations tools that automate classification, surface insights, and reduce cognitive load on your team.

Who is AI development for?

AI development delivers value for specific organizational contexts and challenges.

Ideal Fit
1

Startups Building AI-Native Products

Teams where AI is the core value proposition, needing architecture that scales.

2

Companies Adding Intelligence to Existing Products

Organizations seeking thoughtful integration that enhances without disrupting.

3

Enterprises Modernizing Operations

Workflows depending on human judgment at scale that need reliable automation.

4

Organizations with Underutilized Data

Proprietary information that could power better decisions if made accessible.

5

Teams Stuck at Demo Stage

Those who experimented with AI tools but couldn't get to production.

Not the Right Fit If

You need a simple chatbot for your website, want AI for marketing copy only, or expect magic without data. We build production systems, not experiments.

Success Stories

Case Study

Stylecraft

The system gave us back control,” said Collins. “For decades, we accepted the chaos as part of doing business. Now we can see every piece, every movement, every opportunity. It’s completely changed how we operate.

45%
reduction in time spent locating samples
70%
increase in simultaneous project management capacity
Managing Director
Anthony Collins

Read Case Study

We get asked this all the time.

Straightforward answers to common questions about AI development.

Models

What AI platforms do you work with?

We're model-agnostic. OpenAI, Anthropic Claude, Google Gemini, open-source models — the right choice depends on your use case, cost, latency, and compliance requirements.

Reliability

How do you handle AI hallucinations?

Every system includes output validation, confidence thresholds, fallback logic, and human escalation paths. We design for failure modes from the start.

Integration

Can you integrate AI into existing apps?

Yes. Most of our AI work is integration, not greenfield builds. We design AI features to work with your current tech stack and data sources.

Security

How do you approach AI data privacy?

We treat every AI implementation as a security surface. Data handling follows minimum necessary access principles. We support on-premise and private cloud deployments.

Strategy

Product vs. feature — what's the difference?

Adding AI features bolts capability onto existing workflows. Building AI products designs around what AI makes possible — different architecture, interactions, and business models.

How we build AI products.

A proven process for taking AI systems from concept to production.

1

Discovery & Strategy

We start with your business problem, not feature requests. What outcome matters? Where does AI actually add value? This phase produces clear scope, architecture direction, and success metrics.

1-2 weeksStrategy doc
2

Architecture & Design

We design the AI system architecture alongside product experience. Model selection, data pipelines, prompt engineering approach, integration points, error handling strategy.

2-3 weeksTech spec
3

Build & Integrate

Development in sprints with working software delivered continuously. AI components built with extensive testing for edge cases, reliability, and output quality.

4-12 weeksWorking builds
4

Test & Validate

AI systems require different testing. We validate output quality across diverse inputs, test failure modes, measure performance against baselines, and refine based on real results.

2-3 weeksQuality gates
5

Deploy & Optimize

Launch is the beginning. We deploy with monitoring, track performance metrics, and iterate on prompts, models, and logic based on production behavior.

OngoingLive system

The AI stack we work with.

Model-agnostic development with the right tools for your use case.

OpenAI

GPT-4 and GPT-4o for advanced reasoning, embeddings, and production-grade language tasks.

Anthropic

Claude 3.5 Sonnet and Opus for long-context understanding and reliable output quality.

Google Gemini

Multimodal capabilities with Google Cloud integration for enterprise deployments.

LangChain

Orchestration framework for chaining AI operations and building complex agent workflows.

Pinecone

Vector database for RAG systems, semantic search, and knowledge retrieval at scale.

AWS

Enterprise cloud infrastructure with SageMaker for model hosting and Bedrock for managed AI.

Google Cloud

Vertex AI and BigQuery for large-scale AI deployments and data processing pipelines.

Mixpanel

Product analytics for tracking AI feature usage and measuring system performance.

Typical investment ranges.

Pricing depends on complexity, integrations, and data requirements. These ranges reflect typical AI projects.

AI Feature Integration

$10K – $30K

4–10 weeks

Single AI capability added to existing product with model integration and basic error handling.

  • Model integration & prompts
  • One primary data source
  • Basic fallback logic
Most common

AI-Powered App MVP

$30K – $80K

8–16 weeks

Complete application with AI at core, multiple capabilities, auth, and admin dashboard.

  • Multiple AI workflows
  • User authentication
  • Admin dashboard
  • Production monitoring

Full AI Platform

$80K – $200K+

14–28 weeks

Enterprise-grade AI application with complex data pipelines and advanced security.

  • Multiple integrated AI systems
  • Complex data pipelines
  • Advanced security & compliance
  • Scalable architecture

What you get with us

Tailored AI Solutions

Every AI system built for your specific use case. Your data, your workflows, your security requirements. No generic implementations.

Seamless Integrations

We connect AI to your existing tech stack. CRMs, databases, APIs, and internal systems. Intelligence flows where it's needed.

AI & Automation

Beyond chatbots: intelligent document processing, automated classification, predictive analytics, and autonomous workflows that reduce manual work.

Predictable Timeline

AI features in 4-10 weeks. Full applications in 8-16 weeks. We scope conservatively and deliver on committed timelines, not optimistic guesses.

Specialist AI Team

Our team combines product expertise with deep AI knowledge. Prompt engineering, model selection, RAG architecture, and production deployment.

Ongoing Optimization

AI systems improve with usage. We provide monitoring, performance tracking, and iterative refinement to keep your AI effective as requirements evolve.

Ready to Build AI That Actually Works?

We start by understanding your business end to end. The platform we choose to build what you need comes after clarity.

Let's Talk

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LowCode Agency, in action with AI development.

Legal / EdTech

BarEssay — AI-Powered Bar Exam Prep

Built an AI-powered exam prep platform with personalized study plans and instant essay feedback using LLM evaluation.

30% Study time reduction
Instant AI feedback
HR Tech

The Attributes — Leadership Development

AI platform with intelligent assessments, personalized insights, and team dynamics analysis for enterprise clients.

3,000+ Active users
6 mo To full ROI
Nonprofit

CHIIP — AI Funding Platform

Multi-agent AI system automating funding research, opportunity matching, and proposal assistance for nonprofits.

40% Research time saved
70% On-time proposals

We get asked this all the time.

Straightforward answers to common questions about AI development projects.

Most AI features can be integrated in 4-10 weeks. Full AI applications take 8-16 weeks for MVP. Enterprise platforms run 14-28 weeks. Timeline depends on complexity, data availability, and integration requirements. We provide detailed estimates after discovery.

ChatGPT is a general-purpose tool. Custom AI is built for your specific use case with your data, workflows, and users. Custom systems can be embedded in applications, access private information, follow your business rules, and deliver consistent branded experiences.

Depends on the use case. For RAG systems, you provide the documents and data sources. For most LLM applications, we use pre-trained models and engineer prompts and workflows rather than training new models. We'll identify data requirements in discovery.

Fixed-price engagements based on scope defined in discovery. We don't charge hourly for development work. Pricing reflects complexity, timeline, and ongoing support requirements. We're transparent about what drives cost and where tradeoffs exist.

AI systems need ongoing attention. We offer support and optimization retainers for monitoring performance, refining prompts, updating models, and adding capabilities. We also provide complete handoff to internal teams with documentation and training.

We set performance baselines and success metrics before building. Development includes iterative testing and refinement. If a system isn't meeting targets, we diagnose and address during development, not after launch. Our process surfaces problems early.

We build with guardrails by default — content filtering, output validation, human oversight where appropriate. We discuss ethical considerations in discovery and design systems that can be audited and explained. We don't build systems designed to deceive.