LLM applications
Chat assistants, content generation, code assistants. Function calling, structured output, streaming.
- Chat & copilot interfaces
- Structured output (JSON mode)
- Tool / function calling
- Streaming + SSE
LLM applications, RAG architecture, multimodal agent teams and brand-specific fine-tuning. We turn your data from a pile of files into an engine that drives decisions every day.
Our AI Solutions service isn't a single model call; it's architecture, data, evals and security working as one.
Chat assistants, content generation, code assistants. Function calling, structured output, streaming.
The art of feeding company knowledge to the model. Chunking, embeddings, hybrid search, reranking.
Multiple agents working together. Orchestration, supervisor and planner-executor patterns.
For when stock models aren't enough. Brand-specific models with LoRA, DPO and instruction tuning.
You can't manage a system you can't see. Automated tests, LLM-as-judge, production tracing.
Prompt injection, PII leakage, jailbreaks. Defense for production AI.
A system that flows from data to decisions. Each layer has its own architecture and its own observability.
Everything the model knows comes from here.
The intelligence. Frontier or fine-tuned, orchestrated by task.
Answers, actions, integrations. Wherever the user is.
Click your industry to see concrete use cases and the results achieved.
A chat assistant that finds the right product in a 250K+ SKU catalog, recommends sizes and fits, and resolves returns and exchanges automatically. Smooth handoff to the agent console.
Analysis of contracts, balance sheets and financial reports. Risk scoring, anomaly detection, regulatory compliance checks. An engine that cuts what takes a human auditor hours down to minutes.
Automatic clinical notes after patient consultations, ICD coding suggestions, follow-up plans. KVKK + KHGM compliant. Drafts go to a healthcare professional for approval.
An assistant that analyzes hundreds of pages of contracts clause by clause, flags risk points and drafts negotiation notes. Every claim cites its source.
An assistant that measures each student's real level, pinpoints gaps and generates personalized exercises. A class-wide view in the teacher dashboard.
From frontier models to vector stores, from eval suites to cost dashboards.
An anonymized but real AI Solutions project: the support revolution at an e-commerce company with 250K+ products.
Calls/tickets per agent had reached unsustainable levels. SLAs were slipping, and customer churn rose 14% over 12 months. Most questions were repetitive information requests.
A RAG system over the catalog, order history and returns policy. Real actions through function calling (start a return, track a shipment). Agent handoff rules.
Live in 4 months. Agent workload fell 48% in the first 30 days and the reduction reached 63% by day 90. Customer satisfaction rose from 3.9 to 4.7.
Three starter packages, depending on your needs. All lead to the same place: a live system that drives business.
You have AI ideas but don't know which ones are worth it. Together, we pick the winner in 5 days.
You've picked a use case and want it live. One package, from workshop to rollout.
You have several live AI systems. Continuous development, optimization and new use cases.
We aren't tied to a single model; we orchestrate by task. Claude for creative tasks, Haiku/Sonnet or GPT-4o-mini for fast, low-cost tasks, Gemini for multimodal. Once a model is called, a fallback chain is in place. All of these decisions are observable in the dashboard.
No. When we use Anthropic, OpenAI and Google enterprise / API tiers, data is not used for training (contractually). For sensitive data, we prefer open-source models we can deploy on-prem (Llama 4, Mistral, Qwen).
Three layers of defense: (1) context is mandatory through RAG, (2) structured output + JSON schema validation, (3) every answer is checked with LLM-as-judge. Every claim comes with a citation. The target in production is 99%+ accuracy every month.
Every request is classified: simple queries go to the Haiku/Mini tier, complex ones to Opus/GPT-5. Prompt caching is used. A monthly budget cap is set, with an alert at 80%. You get a detailed cost dashboard.
Yes. CRM (Salesforce, HubSpot, Pipedrive), ERP (SAP, Netsuite), helpdesk (Zendesk, Intercom), communication (Slack, Teams, WhatsApp): all standard integrations. REST/GraphQL/Webhook connections for custom systems.
During the pilot phase, we make a go/no-go decision at the end of every sprint. Success criteria are written into the contract. If we don't hit the KPI, you get a free pivot or a refund. To date, this has happened 3 times across 240+ projects, and all 3 pivots succeeded.
Tell us about your project. In a 15-minute call, we'll choose the best approach together. We reply in under 24 hours.