Custom LLM application development
We build retrieval, tool-assisted workflows and focused AI applications with explicit quality, cost and review criteria. The model is one part of the system—not the product by itself.
“Which contract terms apply to this renewal, and where are they documented?”
Start with the decision
We first test whether language ambiguity, scattered knowledge or tool coordination is actually the bottleneck. A deterministic workflow is often cheaper and more reliable.
If an LLM adds no measurable value, we say so before a pilot consumes budget.
A strong fit
Use a simpler system
Architecture choices
“Custom” rarely means training a foundation model from scratch. The right solution may be conventional automation, retrieval, controlled tool use or targeted fine-tuning.
No model required
Rules, templates and APIs are the better choice when inputs and expected outputs are already structured.
RAG
The application retrieves relevant passages at request time and requires the answer to cite them.
Controlled actions
The model can select approved tools, while authorization, validation and consequential actions remain outside the model.
Evidence required
Fine-tuning can improve a narrow behaviour or output format, but it does not replace retrieval, permissions or application controls.
Example systems
These are solution patterns, not invented case studies. Each starts with a bounded task and makes the control point explicit.
Draft a response from product documentation, account context and known incident notes without exposing material the agent may not access.
Extract relevant terms, compare them with policy and route ambiguous clauses instead of forcing a confident answer.
Answer operational questions and prepare an approved system action without giving the model unrestricted write access.
Quality before launch
A representative evaluation set turns “the answers look good” into a release decision. It also exposes regressions when prompts, sources or providers change.
Each row shows what is measured and why it matters.
Data and operation
Hosting and provider choices vary. What should not vary is clarity about access, retention, model changes and operational responsibility.
The operating model remains explicit across every deployment option.
Data location, model ownership and vendor terms are documented for the selected architecture instead of being promised in the abstract.
Delivery model
We reduce uncertainty in sequence. The first milestone is a measured vertical slice, not a broad AI platform.
Define the workflow, baseline, risk level, owner and the simpler alternatives.
Assemble representative examples, source permissions and acceptance criteria.
Connect the minimum data and tools needed to test the full path.
Measure quality, cost and latency; then add monitoring, review and rollback.
Practical questions
The answer depends on the workflow and risk, but the decision should be explicit before implementation.
Usually not. We first test a suitable foundation model with retrieval, tools and application controls. Fine-tuning is considered only when measured evidence shows a stable gap.
That depends on the selected model and operating requirements. We compare managed APIs, private cloud and self-hosted options against data boundaries, cost, latency and maintenance effort.
A bounded workflow can often be evaluated within several weeks when source data, owners and review examples are available. We confirm scope only after the data and integration path are understood.
We constrain the task, ground answers in approved sources, require citations where appropriate and design fallbacks for uncertainty. Evaluation measures the remaining failure modes; no control makes a model infallible.
Your data remains yours. Ownership and usage rights for application code, prompts, evaluation sets, fine-tuned weights and third-party models are documented for the selected stack and contract.
