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AI that ships to production — not just demos.

We design, build, and deploy LLM applications, AI assistants, and deep-learning models that plug into your real systems and deliver measurable results.

The problem

Sound familiar?

Every team is experimenting with AI, but very few have anything in production. Prototypes stall because of hallucinations, security concerns, or systems that don't integrate with existing tools — while support queues grow and competitors automate.

The gap isn't ideas. It's engineering: evaluation, guardrails, integration, and monitoring. That's the part we do.

What we deliver

Everything this practice covers.

LLM applications & RAG assistants

Chat and search over your own documents, products, and data — grounded, cited, and evaluated.

AI chatbots for web & WhatsApp

Customer-facing assistants that resolve queries and hand off cleanly to humans.

Document intelligence

Extract, classify, and summarize invoices, contracts, and reports automatically.

Deep learning models

Custom vision and NLP models where off-the-shelf APIs fall short.

Model evaluation & guardrails

Test suites, safety filters, and monitoring so quality doesn't degrade silently.

Integration with your stack

Connected to your CRM, helpdesk, database, and internal tools — not a silo.

OpenAI Anthropic Claude LangChain Hugging Face PyTorch pgvector / Pinecone FastAPI AWS / GCP

Our process

How an engagement runs.

01

Use-case discovery

We identify where AI creates real ROI for you — and where it doesn't.

02

Prototype in weeks

A working proof-of-concept on your data, typically in 2–3 weeks.

03

Evaluate & harden

Accuracy testing, guardrails, security review, and cost optimization.

04

Deploy & monitor

Production rollout with dashboards, alerts, and continuous improvement.

Sample 78%

faster first response in our sample AI support-assistant build. See how an AI assistant transforms a support team — read the full sample case study.

Read case study

Questions we hear often

Straight answers.

Will our data be safe?

Yes. We design for data privacy from day one — your data stays in your infrastructure or an isolated environment, with access controls and no training on your private data without written consent.

Which AI model will you use?

Whichever fits the job and budget. We're model-agnostic: OpenAI, Anthropic Claude, or open-source models hosted privately — chosen after testing on your actual use case.

How long does an AI project take?

A working prototype typically takes 2–3 weeks. Production deployment usually lands between 6 and 12 weeks depending on integrations and compliance needs.

What does it cost to run?

We optimize for running cost, not just build cost — caching, model routing, and right-sizing typically cut monthly AI bills by 30–60% versus naive implementations.

Ready to talk about ai & generative ai?

A 30-minute call is enough to tell you whether this is the right move, what it would take, and what it would cost.

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