AI services
AI development services that ship, not just demo
Plenty of AI demos impress in a meeting and then never reach production. We build AI that ships: LLM apps, AI agents, machine learning, and the data engineering that makes them reliable, secure, and worth the spend, for healthcare and e-commerce teams.
The problem
Why most AI projects stall
The demo works, then reality hits. The data is messy or siloed, the model is confidently wrong, no one owns it in production, and the ROI was never defined. Most AI efforts stall not because the model is weak, but because the engineering and data around it were an afterthought.
We start from the outcome you need and the data you actually have, then build the pipeline, guardrails, and integration to reach it. The result is AI you can put in front of real users and measure, not a prototype that quietly dies after the pilot.
What we build
AI capabilities we deliver
Generative AI and LLM apps
Assistants, copilots, and retrieval-augmented (RAG) tools that answer from your own data, safely and accurately.
AI agents and agentic workflows
Autonomous assistants that take real actions across your tools and data, with guardrails and human approval where it counts.
Machine learning and predictive models
Forecasting, scoring, recommendations, and risk models built on your data and validated against real outcomes.
Data engineering and pipelines
Clean, connected, well-governed data pipelines, the foundation that makes AI reliable instead of a demo.
AI integration into your product
Add AI to existing apps through clean APIs and guardrails, without rebuilding what already works.
Intelligent automation
Automate document processing, classification, and decision-heavy workflows to cut manual effort and error.
NLP and computer vision
Extract meaning from text, documents, and images, from clinical notes to product catalogs.
How we work
Our AI development lifecycle
Discovery & feasibility
We define the use case, the success metric, and the ROI, then check the data and constraints can support it. If AI is not the right tool, you hear it here, before the spend.
Data & pipelines
We audit, clean, and connect your data and build the pipelines and features the model needs. For LLM work, this is where we structure your knowledge for retrieval (RAG).
Model & prototype
We pick the right approach, prompting and RAG, fine-tuning, or a classic ML model, build a prototype on your real data, and measure it against a baseline before going further.
Evaluation & guardrails
We score the model on a held-out evaluation set, red-team LLMs and agents for failure and misuse, and add guardrails, human approval, and safety and bias checks.
Integration & deployment
We productionize it with the APIs, security, and MLOps to run inside your product, then deploy behind the access controls and rollout plan you need.
Monitoring & improvement
In production we track accuracy, drift, latency, and cost, capture feedback, and retrain or tune as your data and needs change, so the system keeps earning its place.
Proof
AI and ML in production
Applied machine learning we have already shipped in demanding, real-world settings.
Built responsibly
AI you can put in front of real users
We treat security, privacy, and human oversight as part of the build. Data is handled to HIPAA standards where it applies, models are evaluated before launch, and people stay in the loop on decisions that matter.
FAQ
AI development, answered
Our data is messy or scattered. Can you still build useful AI?
Usually, yes. Messy or siloed data is the norm, not a blocker. We assess what you have first, then do the data engineering to clean, connect, and prepare it so the AI is grounded on data you can trust. If the data genuinely is not there yet, we tell you before you spend on a model.
How do you keep the AI accurate and stop it from hallucinating?
We ground models on your own data through retrieval rather than guesswork, evaluate them against real examples before launch, and keep a human in the loop on decisions that matter. For agents, we add guardrails and approval steps so they act within limits you set, not on their own.
Should we build custom AI or just use an off-the-shelf tool?
Off-the-shelf is the right call for common, generic tasks, and we will say so. Custom is worth it when the AI needs your data, your workflows, or your compliance rules, or when it becomes a real competitive advantage. We help you make that call honestly rather than sell you a build you do not need.
What does an AI project cost, and how soon do we see value?
We start with a focused prototype on your real data so you see value before committing to a full build, often within weeks. Cost depends on the use case, the data, and the integration. We define the outcome and how we will measure it up front, so spend is tied to a result rather than open-ended research.
Is our patient or customer data safe, and do you handle HIPAA?
Yes. We handle data to HIPAA standards where it applies, control and audit access, and are deliberate about what data ever reaches a third-party model. Privacy and security are designed in from the start, not added at the end.
Who owns the model and the code, and who maintains it after launch?
You own the code, the models we build for you, and your data. AI is not set-and-forget, so we monitor accuracy and cost in production and maintain the system as your data and needs change, for as long as you want us to.
