LLM applications that answer from your own documents and data, with evaluation, guardrails and cost control built in.
Stack we work in
Figures as of Oct 2026
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We build applications on large language models that search, summarize, draft and answer questions using your own documents, knowledge bases and databases.
Retrieval-augmented generation (RAG) keeps answers grounded in your sources with citations, and evaluation, guardrails and cost limits are part of the build. We work with hosted models such as GPT, Claude and Gemini, or open models such as Llama and Mistral running in your own environment.
A demo on a few documents is easy. Reliable answers across your whole knowledge base are not.
Without grounding and testing, the model fills gaps with confident guesses that users cannot check.
Policies, contracts, tickets and product data live in different tools, formats and permission models.
Legal and security teams block projects when it is unclear where documents go and who can see the answers.
Teams cannot tell whether a prompt or model change made answers better or worse.
Large models on every request make the application slow and expensive as more people use it.
Outcomes
The tasks, users, data sources, model choice and deployment model, agreed before the build.
Connectors, chunking, embeddings and a vector index that stay in sync with your sources and permissions.
The user interface, chat or API your team and systems use, with citations and feedback built in.
Real questions with expected answers, scored automatically and rerun before every release.
Topic and safety limits, personal data handling, caching, model routing and usage caps, documented and configurable.
Tracing dashboards for quality, latency and cost, plus repositories, prompts and a runbook handed over to you.
Answer staff questions on policies, processes and product details from your intranet and document stores.
Draft replies for agents from help articles, past tickets and order data, with sources attached.
Summarize long documents, extract key terms and compare them against your standard positions.
Draft proposals, RFP answers and account briefs from approved content and CRM data.
Ask questions in plain language over reports, spreadsheets and databases and get answers with references.
Check documents and communications against internal policies and flag passages for a person to review.
Built with
Built with
Built with
Built with
We test candidate models and retrieval settings against your documents and questions before choosing, and show you the scores.
Every answer carries source citations and respects access control, and tracing in LangSmith or Langfuse shows what was retrieved and why.
Azure OpenAI, Amazon Bedrock or open models on vLLM and Ollama in your own cloud. The architecture follows your data rules, not a vendor preference.
Caching, routing and model choice are decided up front, with a cost per request estimate before launch rather than a surprise invoice after.
Featured case study
Every business already owns the sensor network it needs. CCTV records everything and tells you nothing — until someone reviews the footage, usually days after the loss.
Read the case study
We agree the questions or tasks the application must handle, who will use it and which documents and systems it draws on. We check data quality, access rules and privacy needs.
You receive Use case brief and data source map
We test candidate models on your own examples for answer quality, speed, cost and data terms, and choose hosted, private cloud or self-hosted deployment.
You receive Model comparison and architecture plan
We build a working prototype on a sample of your content and write an evaluation set of real questions with expected answers, so quality is measured from day one.
You receive Working prototype and baseline scores
We build ingestion pipelines, retrieval, prompts, guardrails and the user interface or API, and connect the application to your identity, document and business systems.
You receive Production-ready LLM application
We release to a pilot group, trace every request, track quality, latency and cost, and tune retrieval and prompts based on real usage.
You receive Monitoring dashboards and tuning report
We add sources, features and model upgrades over time, rerunning the evaluation set before each change goes live.
You receive Support and improvement plan
Tech stack
AI and machine learning
Databases
Backend and APIs
Languages
Cloud and DevOps
Other tools
Next step
When a task needs several steps and tools, an AI agent built on the same models can complete it within limits you set.
Cost depends on the number and type of data sources, how much content must be ingested and kept in sync, the integrations and user interface needed, access control rules and the level of evaluation the risk calls for. We usually start with a fixed-price prototype, then give an estimate for the production build. Model usage and hosting are estimated separately and can be capped. As a guide, a focused AI pilot usually costs $10,000 to $50,000, and a full custom AI build $40,000 to $150,000 or more (USD).
A prototype on a sample of your content often takes a few weeks. A production application with integrations, access control and monitoring typically takes a few months, depending on scope. We agree milestones after the use case review.
Retrieval-augmented generation finds the most relevant passages in your documents or data and gives them to the model with the question. The model answers from those sources and cites them, so answers reflect your content, stay current when documents change and can be checked by the user.
We ground answers in retrieved sources, tell the model to say when it does not know, show citations and test against an evaluation set of real questions before every release. Guardrails block off-topic or unsafe requests, and low-confidence answers can be routed to a person.
Yes. We can deploy open models such as Llama, Mistral or DeepSeek on your own servers or cloud with vLLM or Ollama, or use private endpoints through Azure OpenAI or AWS Bedrock. We compare quality, cost and running effort with hosted options so you can choose.
Usually not at first. Most business use cases are solved with good retrieval and prompts. Fine-tuning helps when you need a consistent style, a specialized format or a smaller, cheaper model for a narrow task, and we recommend it only when tests show a clear benefit.
Documents stay in your environment or approved cloud region, the application respects existing user permissions so people only see answers from content they can access, and connections are encrypted. We use enterprise model services whose business terms do not use your data for training, and mask personal data the task does not need. Data handling is designed around GDPR and the local data protection rules that apply to you.
You own the code and IP we build for you, including ingestion pipelines, prompts, evaluation sets and any fine-tuned model weights. Your documents and vector indexes stay in accounts you control.
Yes. We connect to your document stores, intranet, CRM, ticketing and databases, use your identity provider for sign-in and work in your repositories alongside your developers.
We monitor quality, latency and cost, review failed or low-rated answers, tune retrieval and prompts, add new sources and test model upgrades. This runs on a monthly support plan, or we hand over to your team with documentation and training.
Book a call, and we talk through the goal, the systems involved and any constraints under NDA. You then get a short written proposal with scope, timeline and estimate. A contact who knows the process, access to the systems and data involved, a few real examples of the work, and a decision maker for sign-off. For RAG, a sample of the documents and 30 to 50 real questions with known good answers let us measure quality from week one.
Enterprise AI search lets staff ask a question and get an answer with sources, drawn from every system they are allowed to see.
One search across many sources
SharePoint, Confluence, Google Drive, Notion, ticketing, CRM and file shares indexed into one place.
Answers with citations
A direct answer with links to the source passages, so people can check before they act.
Permissions respected
Results are filtered by each user's existing access, synced from your identity provider.
Hybrid search and reranking
Keyword and vector search combined, then reranked, so exact codes and names are found as well as meaning.
Usage and gap reporting
See which questions get poor answers, so content owners know what to fix or write.
Book a free consultation. We will review your content and use case and suggest a RAG prototype.
