ChatGPT is generic. Your business isn't.
Off-the-shelf AI doesn't know your products, your tone, your history, or your domain. We build LLM applications that are trained on your data, speak your language, and produce outputs that fit directly into your workflows.
What we build
LLM applications that know your domain and fit your workflow.
Custom LLM Applications
Applications built on GPT-4, Claude, Llama, Mistral, or domain-specific models — chosen based on your use case, latency requirements, data privacy needs, and cost constraints. No default stack.
RAG Systems (Retrieval-Augmented Generation)
Your documents, manuals, policies, and knowledge base become queryable. The AI answers from your content — not from the internet — with source citations, so there are no hallucinations you can't trace.
Fine-Tuned Domain Models
When a general model isn't enough, we fine-tune on your historical data. Your AI learns your terminology, your tone, your edge cases, and your domain — producing outputs that match your standards.
Structured Output & Workflow Integration
Every application produces structured, predictable output: JSON payloads, filled forms, CRM updates, classified tickets. The AI fits into your workflows, not the other way around.
Architecture
We pick the right approach for your use case.
Not every problem needs fine-tuning. Not every problem can be solved with prompting. We match the architecture to the problem — not the other way around.
Prompt Engineering
Best when: Task is well-defined and consistent
Fastest to build · Works within model knowledge cutoff · No custom data needed
RAG
Best when: You need answers from your own documents or database
No hallucinations outside your knowledge · Always up-to-date · No fine-tuning needed
Fine-Tuning
Best when: You need the model to behave consistently in a specialized domain
Best for tone, format, and domain accuracy · Requires labeled training data
Agentic + LLM
Best when: The task requires multi-step reasoning and actions
Most powerful · Highest complexity · Requires full observability setup
Use case scenarios
Examples of what we can build together.
Contract analysis that reads 200-page agreements in 40 seconds
A legal services firm was spending 6–8 hours per contract on manual review. We built a RAG-based document intelligence system that extracts key clauses, flags non-standard terms, identifies risk areas, and generates a structured summary. Review time dropped to under 10 minutes per contract.
Internal knowledge assistant that replaced 60% of Tier-1 support tickets
A 900-person company had a support backlog caused by internal questions about HR policies, IT procedures, and product specs. We built a RAG assistant over 3,400 internal documents. Employees get instant, cited answers. The IT support queue dropped by 60% in the first 6 weeks.
Areas of application
Where does this apply in your organization?
Concrete examples of how this capability translates into real business impact by department.
Customer Service
- RAG-powered support knowledge base
- Multilingual intent detection & routing
- AI agent for multi-step ticket resolution
Sales & Marketing
- SEO & social content generation at scale
- Hyper-personalized outreach sequences
- RAG over product catalog for instant quotes
Document Management
- Contract drafting & clause analysis
- Multi-document Q&A and synthesis
- Regulatory compliance scanning
IT & DevOps
- Internal documentation RAG assistant
- Automated code review & PR summaries
- Incident postmortem generation
Finance & Accounting
- Automated P&L narrative generation
- Invoice & contract data extraction
- Audit trail summarization
Human Resources
- Policy handbook Q&A assistant
- Job description & interview kit generation
- Performance review synthesis
Have a use case in mind?
Tell us the problem. We'll recommend the right architecture — RAG, fine-tuning, or agentic — and give you an honest estimate of what it takes to build it right.