AI Search Platform Comparison: Beyond Keyword Matching

AI search platforms combine keyword and semantic retrieval over your content. This guide covers comparing them, drawing on FISTA Solutions' AI enablement retrieval work.

What should the comparison cover?

Six dimensions, tested on your own corpus.

Dimension	What to verify	Why it matters
Hybrid retrieval	Exact terms and concepts	Business content needs both
Permission filtering	Per user, from source systems	Disclosure risk
Connector coverage	Your actual systems	Deployment effort
Relevance tuning	Boosting and weighting controls	Every corpus needs it
Measurement	Evaluate against known answers	Otherwise impression only
Freshness	Index lag behind sources	Stale results

Why does hybrid retrieval matter?

Because business corpora contain both concepts and exact strings.

A user searching for an error code means that code. A user asking how to handle a refund means the concept. Semantic search handles the second and fumbles the first; keyword search does the reverse.

Check how the platform combines them and whether the balance is adjustable. Test with both kinds of query from your real logs. See vector database comparison.

How should permission filtering work?

Per user, derived from source systems, applied at query time.

Documents indexed from a system where access is restricted must remain restricted in search results. That requires the platform to know each user's permissions in each source and apply them live.

Test explicitly per role. A search platform surfacing restricted content is a disclosure that leaves no trace in the source system's audit log. See AI access review checklist.

Why do connectors decide effort?

Because integration is most of the deployment.

A platform with tested connectors to your document store, ticketing system, wiki, and file shares can be indexing within days. One requiring custom integration for each turns a deployment into a project.

Check the specific systems and versions you run, not the category. Connector coverage claims are frequently broader than the tested reality. See AI integration with legacy systems.

What tuning controls are needed?

Boosting by recency, authority, and type, adjustable by you.

Every corpus needs some tuning: recent documents should usually outrank old ones, official policy should outrank a draft, and some sources are more authoritative than others.

Check whether these controls exist and whether you can change them without raising a request. Tuning that requires a vendor ticket does not happen. See reranker comparison.

How do you measure quality?

With a test set of queries and the results that should appear.

Assemble real queries from your logs with the document that should answer each. Measure how often it appears in the top results, before and after any tuning.

Many platforms make this difficult, which means quality is assessed by impression and complaints. That is the capability most worth checking. See RAG quality checklist.

What sets the ceiling?

Corpus quality, as always.

A search platform over contradictory, stale, duplicated content returns contradictory, stale, duplicated results. No retrieval quality compensates.

Audit the corpus before deploying the platform, or the deployment will surface problems the platform cannot fix. See AI knowledge base quality checklist.

How do you run your own comparison?

Build a test set of fifty real queries with the documents that should answer them. Run it against each candidate with your real corpus and permissions applied.

Then test permission filtering per role explicitly. Those two exercises decide it far better than a feature comparison.

What does switching cost later?

Moderate. Content can be re-indexed, but connectors, tuning configuration, and permission mappings are platform-specific.

Keep source content in its systems rather than only in the search index, and re-indexing is a pipeline run.

What do people get wrong here?

Testing with easy queries. Permission filtering assumed. Connector coverage taken from a marketing list. Tuning requiring vendor requests. And no measurement, so quality is assessed by complaint volume.

Is this different from RAG?

They overlap substantially. A search platform retrieves and ranks; a RAG system does that and then generates an answer.

Many search platforms now generate answers too, which makes the distinction mostly about interface. The retrieval quality questions are identical either way. See build vs buy RAG.

Which should you choose?

Test hybrid retrieval and permission filtering on your own corpus with a real query test set. Weight connector coverage heavily, since it decides deployment effort, and confirm you can measure result quality rather than assess it by impression.

What should you do first?

Assemble fifty real queries with the documents that should answer them. That test set makes every subsequent search decision measurable.

How FISTA Solutions helps

FISTA Solutions builds and operates production AI systems through AI agents, AI enablement, and forward deployed engineering: search tested with a real query set against the client's own corpus and permissions, with measurement capability confirmed before deployment, decisions documented with their reasoning, and handover that leaves your team able to maintain what was delivered. The record is 150+ projects for 50+ companies across 12+ countries.

To run this comparison against your own workload, message FISTA on WhatsApp, or read RAG quality checklist.