AI Development & LLM Integration.
We integrate artificial intelligence where it changes a product's economics: assistants that answer from your own data, agents that complete multi-step workflows, models that score risk or detect fraud, and automation that removes manual steps from operations. We work with commercial LLM APIs and open-source models alike, and we measure results against business metrics rather than demo wow factor.
Discuss your projectProducts we build.
Specific products we have shipped more than once, with scope, timelines and budgets you can plan around.
Where AI pays off.
Customer support and onboarding assistants grounded in product documentation. Finance and trading copilots that explain data and draft actions for human approval. Document processing — KYC files, invoices, contracts — with extraction and validation. Predictive models for churn, credit risk and fraud. Internal tools that turn natural-language questions into queries over company data. The common requirement is reliability: hallucination control, guardrails, evaluation and cost management.
How we ship AI features.
We begin with a short feasibility phase: a dataset review, an evaluation set and a prototype against real inputs. Production systems use retrieval-augmented generation over vector and relational stores, structured outputs with validation, tool calling with explicit permissions, and human-in-the-loop checkpoints where the stakes are high. Everything is observable — prompts, tokens, latency and quality scores — so you can see cost and accuracy per feature. Front ends are built with Next.js and the Vercel AI SDK for streaming experiences.
Delivery process.
- 01
Use-case and data assessment: value, risk, available data and an evaluation set with target quality
- 02
Prototype in 2–3 weeks against real inputs; go/no-go based on measured accuracy and cost
- 03
Production build: RAG pipelines, guardrails, monitoring, fallbacks and integration into your product
- 04
Continuous evaluation, prompt and model updates, fine-tuning when it beats prompting
What We Offer
- 01
Machine Learning Models
- 02
Natural Language Processing
- 03
Computer Vision
- 04
Predictive Analytics
- 05
AI Chatbots
- 06
Process Automation
Technology stack
- OpenAI, Anthropic, Google and open-source models (Llama, Mistral)
- Vercel AI SDK, LangChain, LlamaIndex
- pgvector, Pinecone, Qdrant
- Python, PyTorch, scikit-learn, FastAPI
- Evaluation and tracing tooling, prompt management
- Next.js, TypeScript, streaming UIs
Frequently asked questions.
01What is the difference between LLM integration and training a custom model?
Most products do not need training. Retrieval-augmented generation and good prompting over a commercial or open-source model solve the majority of cases at a fraction of the cost. We fine-tune or train models only when evaluation shows a clear gain, for example in classification or domain-specific extraction.
02How do you keep our data private when using AI APIs?
We use enterprise API tiers with no-training guarantees, redact sensitive fields before calls, keep embeddings and documents in your infrastructure, and can deploy open-source models in your cloud or on-premises when required.
03How do you prevent hallucinations?
Grounding answers in retrieved sources with citations, constraining outputs to schemas, verification steps for critical actions, evaluation sets that run on every change, and clear fallbacks to humans when confidence is low.
04How much does an AI assistant cost to build?
A grounded support or product assistant with a knowledge base, analytics and an embeddable widget typically costs 20–40k USD for the first release, plus model usage that we help you forecast and cap.
05Can you add AI features to a fintech product?
Yes, this is one of our core combinations. Examples include a finance assistant that explains transactions and portfolios, document processing for onboarding and anomaly detection on transaction streams, all with audit trails suitable for regulated environments.
Have a project in mind?
Send us a short brief. Within 24 hours you will hear back from an engineer, not a sales script, with questions, a rough scope and a suggested next step.