LLM platforms
The model is the commodity. The system around it is the product.
LLM copilots and assistants that answer from your own material and survive a security review. Your data stays in your infrastructure. Switching provider is a config change, not a rebuild.
Your vision.
Powered by LLM platforms.
LLM Product Strategy
Decide which use cases are worth building, what a good answer looks like, and how you will know whether it worked before anyone writes a prompt.
Use Case Discovery
Map your customer journeys and internal processes to find where an assistant saves real hours, and where it would just be a novelty.
Conversation and Experience Design
Flows, prompts, fallbacks, and interface, designed so the system is honest about what it does not know rather than confidently wrong.
LLM Application Engineering
The full build: chat surfaces, in-product copilots, retrieval over your own documents, and agents that can act inside your systems.
Orchestration and Tuning
Retrieval pipelines, model routing, and prompt tuning for your domain, your security constraints, and a running cost you can defend.
Evaluation and Monitoring
Quality, latency, and cost tracked in production, with an evaluation set that catches a regression before your users do.
What a working platform changes
- Cut support load and response time with an assistant that answers from your actual documentation.
- Surface the right document, record, or next action in plain language instead of a search results page.
- Put it inside the tools people already use, so nobody has to visit a separate chat window.
- Ship with access controls, redaction, and tracing in place, so security review is a step rather than a wall.
Reference architecture
What an LLM platform is actually made of.
Most prototypes are the top two layers and nothing underneath. That is why they demo well and never go live.
Exhibit 1
The five layers of a production LLM platform, and who owns each one.
- 01
Surfaces
Where people actually meet it.
- In-product copilot
- Chat interface
- Slack and Teams
- API
- 02
Orchestration
Deciding what to do with a question.
- Model routing
- Tool calling
- Agent workflows
- Conversation memory
- 03
Retrieval
Grounding answers in your material rather than the model’s guesswork.
- Chunking and embeddings
- Vector store
- Re-ranking
- Citations
- 04
Guardrails and evaluation
The layer that decides whether it is allowed to go live.
- Access control
- PII redaction
- Evaluation sets
- Tracing and audit logs
- Cost ceilings
- 05
Your systems
The foundation. Yours before, during, and after.
- Documentation
- Tickets and CRM
- Product database
- Data warehouse
The layer teams skip is the fourth. It is also the only one that decides whether legal, security, and support will let the thing go live.
Layers one to four are what we build. Layer five is yours and stays yours, in your accounts, throughout.
Our approach
Problem & Data Discovery
We find the use case worth building and check whether your data can actually support it.
You leave with
- A scored shortlist of use cases
- An honest data readiness read
- The one we recommend, and why
LLM App Build & Testing
The build, with an evaluation set written before the prompts rather than after the complaints.
You leave with
- A working application
- An evaluation set you own
- Guardrails in place before launch
Stack & Workflow Integration
It lands inside the tools people already use, not in a separate window they have to remember.
You leave with
- Integrated with your existing tools
- Access rules mapped to your roles
- Runbooks for your team
Monitoring & Continuous Tuning
Real conversations tell you what to fix. We instrument for that from day one.
You leave with
- Quality, latency, and cost dashboards
- A tuning loop from real usage
- A monthly review of what changed
How we engage
Three ways to work with us.
Which one fits depends on whether you are still choosing the use case, building the first one, or keeping a live system honest.
Discovery sprint
Best when you are confident AI should help somewhere but not confident where, and want that settled before spending.
What is included
- Use case mapping across journeys and internal processes
- A data readiness assessment, including what is missing
- A shortlist scored on value against feasibility
- A build plan and cost envelope for the top candidate
Fixed price for a defined window.
Production build
Best when the use case is chosen and it needs to work for real users under a real security review.
What is included
- The full build across layers one to four of Exhibit 1
- Evaluation set and guardrails in place before launch
- Integration with the tools your teams already use
- Handover with runbooks and everything in your accounts
Fixed price, agreed after discovery. Billed against milestones.
Run and improve
Best when it is live and the job becomes keeping it accurate, affordable, and current as models change.
What is included
- Quality, latency, and cost monitored in production
- Retrieval and prompts improved from real conversations
- Provider and model changes handled without a rebuild
- A monthly review of what changed and what it cost
Monthly retainer. Rolling term, cancellable with notice.
How we arrive at a number
Build cost depends on the use case and running cost depends on volume, so a published rate would be wrong for you either way. Instead: a 30 minute call at no charge, then a written scope covering the build and an estimated monthly running cost you can take to finance. Nothing starts until you agree to both.
Fit
Whether this is right for you.
A good fit if
- You have real volume of documents, tickets, or conversations to work from
- There is a specific job you want done, rather than a general wish to use AI
- Someone internally can tell us what a good answer looks like
- It needs to survive a security review, not only a demo
- You are prepared to measure whether it actually worked
Probably not a fit if
- The goal is a demo for a board meeting rather than something people use daily
- Your content is scattered and nobody is willing to tidy any of it
- The task must be right every single time with no human in the loop
- You want an assistant mainly because a competitor announced one
- Nobody will own it once we hand it over
Insights
- ArticleHow LLM Apps Integrate Into Real Workflows - CRMs, Dashboards, Ticketing Systems, and MoreLearn how embedding LLM assistants inside existing tools creates frictionless workflows that boost productivity without changing how teams work. Nov 28, 2025
- ArticleThe Hidden Power of Knowledge Intelligence: How Companies Use LLMs to Reduce Support Load by 60%A breakdown of how AI copilots and knowledge layers automate tickets, FAQs, and internal queries to drastically improve response time. Nov 28, 2025
- ArticleWhy Every Modern Business Needs a Custom LLM Instead of a Generic ChatGPT IntegrationDiscover how custom LLM platforms turn your internal data into precise, secure, business-ready intelligence that generic AI tools can’t deliver. Nov 28, 2025
Common questions
Answered before you have to ask.
Where does our data go, and is it used to train models?
It goes to whichever provider we agree on, under a commercial agreement where prompts and outputs are not used for training and are retained only briefly for abuse monitoring. Your source documents stay in your own infrastructure. If a use case cannot leave your network at all, we will say so during discovery rather than after the build.
Which models do you use, and are we locked in?
We are provider agnostic and pick per use case on quality, latency, and cost. The orchestration layer exists partly so that switching provider is a configuration change rather than a rebuild, which matters more than usual in a market that reprices every few months.
How accurate will it be?
We will not quote a percentage before seeing your data, and be wary of anyone who does. What we do is build an evaluation set from your real questions, measure against it before launch, and keep measuring after. Accuracy is a number you own and watch, not a promise made in a proposal.
Do we need a lot of data, or to fine tune a model?
Usually neither. Most business use cases are solved by retrieval over documents you already have, which needs no training and stays current as those documents change. Fine tuning is occasionally the right answer and is expensive to maintain, so we treat it as a last resort rather than a starting point.
What does it cost to run each month?
It scales with usage rather than being fixed, which is unfamiliar if you are used to licensing software. We design to a cost envelope you set, put ceilings in place so a runaway loop cannot produce a surprise invoice, and give you an estimate you can take to finance before the build starts.
What happens when the models change again?
They will, roughly every quarter. Because routing sits behind the orchestration layer, moving to a better or cheaper model is a change we make and evaluate rather than a project you fund again. That is most of the argument for building the middle layers properly.
