Perplexity

Traditional language models answer from what they learned during training. That knowledge has a cutoff date and cannot reflect recent events, updated regulations, or new information.

Perplexity takes a different approach:

Web search for relevant sources. It retrieves current information rather than relying only on training data.

Synthesis into an answer. It turns multiple sources into a coherent response.

Citations for verification. Users can trace claims back to sources.

Continuous recency. It remains current as the web updates.

This makes Perplexity particularly useful when accuracy and recency matter.

Perplexity suits applications where:

Current information matters: Regulatory changes, market developments, recent events. Answers need to reflect the world as it is now.

Verification is important: Professional contexts where users need to check sources. Citations build confidence.

Broad knowledge is required: Questions spanning many topics where no single knowledge base suffices.

Research efficiency matters: Synthesising information from multiple sources into coherent summaries.

Perplexity offers API access for building applications:

Sonar models provide search-augmented responses through a straightforward API. Suitable for adding research capability to existing applications.

Citation handling returns source information that your application can display or process.

Customisation allows specifying search focus and response characteristics.

We integrate Perplexity capability into chatbots, internal tools, and custom applications.

For higher-trust use cases, we typically add governance around sources. That can include preferring authoritative domains, separating “research mode” from “answer mode”, storing citations for audit, and making it easy for users to verify the underlying references before decisions are made.

Perplexity adds value in specific scenarios:

Customer service: Answering questions about current offers, recent changes, or external information affecting customers.

Internal knowledge: Helping employees find information from internal and external sources together.

Research tools: Applications supporting research, competitive analysis, or market monitoring.

Compliance and regulation: Staying current with regulatory requirements and guidance.

Perplexity is powerful but has characteristics to understand:

Search quality matters. Results depend on source availability and the quality of retrieval.

Conflicts are normal. Different sources may disagree; the system should surface uncertainty rather than hiding it.

Latency is a trade-off. Search adds a step, so responses can be slower than a model answering from context alone.

Reliability needs design. You need guardrails, source preferences, and fallbacks when retrieval is weak.

We design applications that work well with these characteristics.

We help organisations add Perplexity capabilities:

Evaluation: Determining whether search-augmented AI fits your use case.

Integration: Building Perplexity into applications with appropriate user experience.

Source handling: Displaying citations effectively and enabling verification.

Fallback design: Handling cases where search does not find useful information.

Use these prompts to decide whether search-augmented AI is the right approach for your use case.

“Which parts of this problem require current external information, and which can rely on internal sources?”

“What sources should we trust, and how should we handle conflicting information?”

“What experience do users need: citations, summaries, or a guided research workflow?”