Multi-agent systems
Orchestrated agents that plan, call tools, and verify each other, used for research, analysis, document processing, and decision support.
ServiceLast updated
Custom AI Solutions
Get the AI system no SaaS vendor will sell you; RAG pipelines, multi-agent orchestration, AI-powered internal tools.
Service overview
Get the AI system no SaaS vendor will sell you. We build RAG pipelines, multi-agent orchestration, fine-tuned models, and AI-powered internal tools that fit your workflow exactly. The result is capability your competitors cannot buy off the shelf, on infrastructure you own.
Engagement
What this service includes.
Discovery, architecture, evaluation plan
Build, integrate, deploy
Documentation, training, handover
What we offer
Why this service matters.
RAG pipelines
Production retrieval systems with reranking, hybrid search, and continuous evaluation so the answers stay accurate as your data grows.
Internal AI tools
Custom interfaces that wrap your own data and processes in an AI-first UX, so your team works in natural language instead of forms.
Custom AI integrations
Powerful AI integrations.
We pick the right model for your custom ai build, then blend providers behind a single internal interface.
OpenAI
GPT and embeddings. Broad ecosystem, strong structured-output and tool use, the safest default for general production.
Claude
Anthropic's frontier model. Our default for agents and long-context work where reasoning matters more than raw speed.
Gemini
Google's long-context multimodal family. Excellent for document and video pipelines, especially at scale.
Grok
xAI's model with live-web reasoning and a different blend of strengths. Useful for research-style and edge-case workloads.
DeepSeek
Open-weight models with strong cost-to-performance. We use it self-hosted when residency or unit economics demand it.
Perplexity
Citation-grounded search API for live-web augmented agents. Drops cleanly into RAG pipelines that need fresh sources.
Workflow ideas
Patterns we have built in custom ai.
Concrete workflows we have documented in this area. Each one ships behind your stack with the same engagement model as the service above.
FAQ
Questions about custom ai.
When should we build custom vs use an off-the-shelf tool?
If a SaaS product fits 80% of the workflow, use it and integrate. Build custom when you need workflow logic that no vendor will give you, when data sensitivity rules out third parties, or when the per-seat economics break at scale.
Do you fine-tune models or just use prompt engineering?
Both, depending on the problem. Most use cases are solved with strong retrieval and careful prompting. Fine-tuning earns its place for narrow, high-volume tasks where output style or domain language matters.
What about data privacy and where the data goes?
We default to running inference inside your tenancy with self-hosted or private-deployment models when sensitivity demands it. We document the data flow before any code is written.
What stack do you build on?
Python and TypeScript, LangChain or LlamaIndex when they help, direct SDK calls when they do not, FastAPI or Next.js for surfaces, Postgres with pgvector for retrieval. Pragmatic choices, not a fixed template.
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