Research domain
LLM Optimization
Inference optimization, token efficiency, prompt engineering, context compression and enterprise-grade orchestration for large language models.
Large language models are powerful but expensive and slow when used naively at enterprise scale. Our LLM optimization research looks for ways to deliver the same or better answers with fewer resources, so organizations can serve thousands of users on their own infrastructure without runaway costs.
Why it matters
For governments and large enterprises, the cost and speed of AI decide whether a pilot ever reaches every employee. Running models on your own infrastructure makes efficiency even more important, because hardware is finite and must be shared fairly across departments.
Questions we are working on
How do we keep answer quality high while cutting the tokens each request uses?
Which models should handle which tasks, and when should a request move to a larger model?
How much context does a model really need, and how can we compress the rest?
How do we serve open models efficiently on-premises and in private clouds?
Focus areas
Inference optimization
Batching, caching, quantization and serving configurations that lower latency and hardware needs for self-hosted models.
Token efficiency
Measuring and reducing the tokens spent on prompts, retrieved context and outputs without losing accuracy.
Prompt engineering
Structured, testable prompt patterns that behave consistently across models and versions.
Context compression
Summarizing and selecting context so long documents and conversations fit within budget.
Model routing
Sending each request to the smallest model that can answer it well, with escalation when needed.
Orchestration
Coordinating multiple model calls, tools and checks inside one reliable enterprise workflow.
Our research approach
01
Measure the baseline
Profile real workloads for tokens, latency, hardware use and answer quality before changing anything.
02
Experiment systematically
Test prompts, context strategies, models and serving settings against fixed evaluation sets.
03
Route and compress
Match each task to the smallest capable model and send only the context it needs.
04
Monitor in production
Track cost and quality continuously so savings never come at the expense of accuracy.
What it delivers
Lower cost per answer
Fewer tokens and smaller models for routine requests.
Faster responses
Caching, batching and tuned serving reduce waiting time.
Predictable budgets
Usage controls keep spending within agreed limits.
Scale on your hardware
Serve more users from the same on-premises capacity.
Milestones
2023
Baseline studies of enterprise LLM workloads and prompt patterns.
2024
Model routing and context compression experiments.
2025
Cost-efficient designs deployed for large employee assistants.
2026
Optimization controls built into BrainShift AI Gateway.
In practice
- Cost-aware design used in the AI HR companion serving 14,000 employees across 5 countries.
- Model routing and usage controls built into BrainShift AI Gateway.
Related BrainShift products
Frequently asked
Does optimization reduce answer quality?
Every change is tested against evaluation sets first; changes that lower quality are not adopted.
Does this work with open models?
Yes. Much of the work targets self-hosted open models running in private environments.
Can we see where our AI spending goes?
Yes. Usage is tracked by application, team and model so costs are visible.
Other research domains
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