GenAI
Generative AI for product and operations teams
shyash.ai, our own product: AI agents write the draft, and a person approves it before it goes out.
shyash.ai, our own product: AI agents write the draft, and a person approves it before it goes out.
- Your team
- Answers drawn from your own documents
- Your product
- Drafting, summaries and search, built in
- Where it runs
- Private models inside your own infrastructure
- Our product
- shyash.ai: AI agents, a person approves
When teams call us about AI
Each row links to how we handle it.
- Staff spend hours a day retyping back-office documents.How we approach it
- The AI pilot never made it from demo to production.How we approach it
- Company data must stay inside your own infrastructure.How we approach it
- You are choosing: Copilot seats or a document pipeline.How we approach it
- There is no ML team to run the system after launch.How we approach it
- You are sizing GPU servers for a private language model.How we approach it
What generative AI does for your team
Inside your own infrastructure
- 01Your documentsPolicies, contracts, manuals and tickets, kept where they already are.
- 02Search indexFinds the passages that answer the question: retrieval-augmented generation (RAG).
- 03Private language modelWrites the answer from those passages on servers you control.
- 04Your teamGets the answer in the tools and systems it already uses.
- 01
Answers from documents
Your team asks a question in plain language and gets an answer drawn from your policies, contracts, manuals and tickets. The system is retrieval-augmented generation (RAG) over your own documents.
Fine-tuning or RAG on company data
- 02
Documents into records
We have taken an LLM integration to production inside a client's FinTech perimeter: extracting and normalising unstructured broker and venue notices — corporate actions, instrument and margin changes — into structured records the trading system consumes.
AI document processing
- 03
Features in your product
Drafting, summaries and search built into the product you already run: the product drafts replies and reports from its own data, shortens long files, and finds records from a plain-language request.
OpenAI API or Anthropic API
- 04
Private language models
We deploy private language models inside your own infrastructure and connect them to the systems you already run. Your documents and customer data stay inside your perimeter.
OpenAI API or a self-hosted model
- 05
Pilots that stalled
A pilot that worked in the demo and never reached production. We take over projects that stalled with another team and bring them to production, inside your infrastructure when the data must stay there.
The AI pilot that never reached production
Our product · shyash.ai
shyash.ai: AI agents research and write, a person approves
- Agents
- They research, write, check and measure
- Memory
- One shared base of company facts and decisions
- Control
- A person approves everything that goes out
Own servers or a hosted API
A private model runs inside your own infrastructure, and the documents stay there. A hosted API from OpenAI or Anthropic needs no servers of your own, and the text goes to the provider. These pages set out the trade-offs and the GPU memory a private model needs.
Next step
Building an AI feature or a document pipeline?
Send a few lines on the documents, the systems the model must connect to, and whether the data may leave your infrastructure.
We'll respond within 24 hours with a clear action plan.
Three ways to work with us
- 1
Full delivery
We take the whole project, from the architecture through the build to the launch.
- 2
A dedicated team
Engineers who work only on your product, for as long as you need them.
- 3
Engineers in your team
Our engineers join your team and work in your process, with your leads.
You keep full ownership of the product and the code, except our reusable components.
You pay for a delivered system or for a team for an agreed period. We do not sell access to a product.
How we start
- 1
Describe your project
A few lines on what you're building and where you're stuck.
- 2
Free project intro call
A 30-minute session to discuss your project and its architecture.
- 3
Project pricing
After the session, we price the work for your project.
- 4
Build with us
Full delivery, a dedicated team, or engineers embedded in your team.
Questions we get asked
What do people mean by generative AI?
Models that produce new text, images or code from an instruction. The large language models behind ChatGPT, Claude and Gemini work with text: they answer questions, write drafts, summarise documents and pull facts out of them.
What can generative AI do for operations teams?
Most of the value is in text-heavy work: answering staff questions from internal documents, drafting replies and reports, summarising long files, and turning notices, forms and emails into structured records for your systems.
Can the model run inside our own infrastructure?
Yes. We deploy private language models inside your own infrastructure and connect them to the systems you already run. We also build retrieval-augmented generation (RAG) over your own documents.
What is shyash.ai and who built it?
shyash.ai is our own product: it shows companies what AI assistants tell their buyers. It runs on AI agents that research, write, check and measure, and a person approves everything that goes out.
What GenAI work do you take on?
Answers from your company's own documents, AI features inside your product, and structured records from documents, on private models inside your own infrastructure. Our own product, shyash.ai, runs on AI agents, and a person approves everything that goes out.
Read before you build
Recent articles
9 min read
Generative AI on Your Own Servers: How Much GPU Memory a Private Language Model Needs
How to estimate the GPU memory a private language model needs: the weights, the memory each request adds, how many GPUs that means and what renting them costs per hour.
10 min read
Fine-Tuning or RAG: Train an AI Model on Your Company's Data or Let It Search Your Documents
To answer questions from your documents, an AI model usually needs RAG, which looks up the facts for each question. Fine-tuning is better for format and tone.
10 min read
No ML Team: Who Runs an LLM System for Support Tickets and Documents on Your Own Servers After Launch
A company with no ML team can leave its LLM system with the firm that built it. The company's own IT team can also take over some or all of the work.
10 min read
Outsource Back-Office Document Work to a BPO Provider or Automate It With AI on Your Own Servers: How a COO Decides
A BPO provider's staff can process your claims and KYC files, or AI on your own servers can read them. Score both on the same sample of your own documents.
Planning generative AI for your team?
We'll respond within 24 hours with a clear action plan.
Free project intro call · 30 minutes