01
Prompt and context engineering
Design reusable instructions, context windows and examples around the task.
LLM engineering and evaluation
LLM systems engineered for quality, cost, latency and control.
Language model systems tuned for quality, cost, latency and control.
We turn model capability into dependable application behaviour through retrieval, structured generation, fine tuning, evaluation and provider aware design.
Measured qualityLower driftControlled costProvider options
BYOND BOUNDRYS CONSULTINGApplied AI
Capability focusLLM Engineering
03Quality dimensions02Provider paths100%Testable outputs
Why this matters
Turn the AI opportunity into a workflow people can actually use.
A good model response is not the same as a good system. Production LLM engineering needs repeatable context, predictable outputs, measurable quality, safe fallbacks and cost discipline.
What we build
A connected blueprint, not a list of disconnected features.
We align experience, intelligence, data and operations around the job your team needs to complete.
02
Structured outputs
Use schemas and validators so model responses can safely feed software.
03
Fine tuning and adapters
Evaluate when focused adaptation is better than a larger general model.
04
Evaluation harnesses
Test accuracy, groundedness, refusal, latency and cost before release.
How we make it dependable
Design principles that keep the system useful after launch.
01Measure the task before choosing the model.
02Use context and structure to reduce variance.
03Test representative cases, not only happy paths.
04Optimise quality, latency and cost together.
How it comes together
A delivery path with visible decisions.
The sequence keeps scope, evidence and ownership clear from the first conversation to the next release.
- 01Baseline
Define the task, rubric and representative set.
- 02Engineer
Shape prompts, retrieval and structured outputs.
- 03Evaluate
Compare quality, latency, cost and failure modes.
- 04Harden
Release the system with monitoring and change control.
Delivery workflowDefine task and quality bar → Build baseline prompts → Add retrieval or structured constraints → Evaluate representative cases → Release with monitoring
What you receive
Useful artefacts, not only advice.
Every engagement is shaped around practical outputs that help your team make a decision, start a build or operate the next version.
01LLM task specification
02Prompt and context design
03Evaluation dataset and rubric
04Provider and cost comparison
05Production integration guidance
Technology layer
The stack follows the workflow.
We choose tools for fit, control and maintainability, not because a logo is fashionable.
Azure OpenAI (GPT-4o)
Google Gemini Flash
Flan T5RoBERTaQwen3 8B / 30B
LangChain
LangGraph
Pydantic
PyTorchHugging FaceReady for the next step?
Bring us the workflow behind the requirement.
We can help shape the first release, architecture and evidence needed to move forward.