AI & ML Solutions
engineered for production.
We build production AI features - not demos. From RAG pipelines and LLM-powered copilots to document intelligence and semantic search, we deliver systems that handle real traffic, real costs, and real evaluation. Our team works fluently with Claude, GPT, and open-weights models, with the infrastructure to fine-tune, evaluate, and deploy. Every engagement starts with a problem definition and an evaluation harness - because shipping an AI feature without measurable quality gates is the fastest way to ship a feature that hallucinates in production.
AI & ML Solutions engagement brief
AI features that ship to production - measured, evaluated, and grounded in your data.
> 90%
Task accuracy on production AI features (vs. eval harness)
< 1.5s
P95 response time on RAG-powered Q&A endpoints
60–80%
Token cost reduction via prompt + model right-sizing
Tech stack preview
Industries served
What we deliver.
- Conversational AI and copilots embedded in your product
- Document intelligence and semantic search
- Custom fine-tuning on Claude, GPT, and open models
- MLOps with versioned datasets and model evaluation
- Agent frameworks with tool-calling and memory
- AI safety, prompt injection mitigation, evals at scale
How a ai & ml solutions engagement runs.
Problem definition & eval design
We define the AI task, success metrics, and an evaluation harness with golden datasets. Without evals, you cannot ship safely.
Prototype with off-the-shelf models
Validate with Claude or GPT-4 to confirm the problem is solvable. We measure quality, latency, and cost before committing to architecture.
Architecture: RAG, fine-tune, or agents
Pick the right approach: retrieval-augmented for knowledge, fine-tuning for style/format consistency, agents for multi-step workflows.
Productionize with guardrails
Rate limiting, prompt injection defense, content filtering, observability, fallbacks, and per-user cost controls.
Continuous evaluation & tuning
Production traffic feeds back into the eval harness. We tune prompts, retrieval, or fine-tunes against measurable quality metrics.
Senior engineering, production-grade outcomes.
Senior squads only
No bait-and-switch. The engineers in your kickoff are the ones writing your code - typically 8–15 years of production experience.
Production-grade by default
Tests, observability, CI/CD, and infra-as-code from sprint one. We do not bolt on quality at the end.
AWS-native engineering
We build on AWS where it makes sense and on managed services where they make you faster. We are pragmatic, not dogmatic.
You own everything
Code, docs, infra accounts, design files, runbooks - handover is a deliverable on every engagement, not an afterthought.
Where ai & ml solutions ships production results.
Healthcare
HIPAA-ready software for telemedicine, EHR integrations, and clinical workflows.
Explore industryFinTech
PCI-aware payment platforms, lending engines, and finance-grade infrastructure.
Explore industryeCommerce & Retail
Headless commerce, conversion-optimized storefronts, and omnichannel platforms.
Explore industryAI & ML Solutions engagement questions.
Should we use Claude, GPT, or open-source?
We default to Claude for reasoning-heavy tasks and GPT-4o for breadth. Open-weights models (Llama, Mixtral) win when you need data residency, low cost at scale, or fine-tuning. We pick per use case in discovery.
How do you handle hallucinations in production?
Three layers: retrieval-grounded prompting (RAG), strict output schemas with validation, and an eval harness that catches regressions before they ship. We never deploy without measurable accuracy gates.
Can you train a model on our private data?
Yes - either via fine-tuning (Claude, GPT, open-weights) or via RAG over private knowledge bases. We run all training in your AWS account, with no data leaving your perimeter.
How do you control AI costs?
Prompt right-sizing, model tier selection per query type, response caching, output streaming, and token-budget alerts. Most clients see 60–80% cost reduction post-tuning.
Do you build agents?
Yes - tool-calling agents with memory, planning, and safety constraints. We typically use Claude for agent reasoning given its tool-use reliability and lower hallucination rates.
Other services
Web Development
Production-grade web platforms that load fast, rank well, and scale without re-platforming.
Mobile Apps
Cross-platform iOS and Android apps that feel native and ship to both stores from one codebase.
Cloud & DevOps
Production cloud infrastructure with zero-downtime deploys, full observability, and runbooks your team can actually use.
Have a product idea or a system to scale?
Tell us what you're building. You'll hear back within one business day - from a senior engineer, not a sales rep.
- Free 30-min discovery call
- Fixed-scope or T&M engagements
- NDA on request - first reply within 24h