Question 1

Is context engineering the same as RAG (Retrieval-Augmented Generation)?

Accepted Answer

Context engineering is broader than RAG. RAG is one technique within context engineering. True context engineering encompasses retrieval, ranking, filtering, curation, feedback loops, and continuous optimization.

Question 2

How much does context engineering improve accuracy?

Accepted Answer

We typically see improvements from 15–40% in accuracy, with some customers achieving 92%+ accuracy on complex tasks that previously had 60–70% accuracy.

Question 3

How does context engineering affect costs?

Accepted Answer

Context engineering typically reduces costs by 50–70% through token optimization. By delivering only high-signal context, you reduce the tokens your model needs to process.

Question 4

What’s the difference between context engineering and fine-tuning?

Accepted Answer

Fine-tuning adapts a model to your domain through training. Context engineering adapts the information flowing into the model through intelligent retrieval and curation. Both have a place, but context engineering is faster, cheaper, and often more effective for enterprise use cases.

Question 5

Can we build context engineering in-house?

Accepted Answer

Yes, but it requires deep expertise in retrieval systems, NLP, and production AI. Most organizations underestimate the complexity. Many companies spend 6–12 months building what we can architect in weeks.

Question 6

How long does it take to implement context engineering?

Accepted Answer

A proof of concept typically takes 2–4 weeks. A production system takes 6–12 weeks depending on data complexity and scale.

The context layer your AI is missing

We build the context layer for long-horizon tasks. Retrieve the right facts, compress the noise, and govern context across tools and agents so outputs are consistent and verifiable.

Why context engineering matters now?

The competitive advantage has shifted from the model to the context.

The AI landscape has shifted. LLMs are commoditized. Every company can access OpenAI, Claude, or Llama. The competitive advantage no longer comes from the model—it comes from how well you engineer the context flowing into that model.

Your competitors are building context engineering capabilities. They're reducing hallucinations, cutting costs, and shipping production systems faster.

The question isn't whether you need context engineering. The question is whether you'll build it in-house or partner with experts who've already solved these problems.

The three failures of one-size-fits-all LLMs

Production LLMs unlock value, but performance, cost, and governance drift at scale. The bottleneck is fit.

Retrieval failure. A context pipeline issue.

Context decay. Performance degrades as your data scales.

Context chaos. A systems problem.

An engineered context layer

Solving these failures requires a disciplined, engineering-led approach. We build a robust system that feeds your AI clean, structured, and relevant information.

Precision retrieval & ranking. Boosting accuracy and user trust.

Precision retrieval & ranking. Boosting accuracy and user trust.

Token compaction. High accuracy while token costs and latency drop significantly.

Token compaction. High accuracy while token costs and latency drop significantly.

Structured context orchestration. Scalable, enterprise-grade verifiable outputs.

Structured context orchestration. Scalable, enterprise-grade verifiable outputs.

Context engineering, proven in practice

Long-horizon tasks need better context. We engineer systems that stay accurate and citation-aware.

Knowledge management

Technical customer support

Deep research & analysis

Compliance & risk management

Persistent agent memory

Customer 360 intelligence

Business impact of context engineering

We engineer your context layer for the outcomes that matter.

30%

<6%

50–90%

4–8 weeks

Up to 80%

Frequently asked questions

Common questions about context engineering and our approach.

Ready to engineer your context?

The difference between AI pilots and production systems is context. Let's discuss how we can help you build a context layer that transforms your AI initiatives into competitive advantages.