Reads what it retrieved
The generative layer sits on top of permission-filtered hybrid search — it reads only the documents the requester is cleared to see, then composes one direct answer.
Elastic Generative AI PartnerElastic Select Partner70+ certified engineers15 billion documents migratedPermission-aware · on-prem/VPC options
Can your team just ask, and find the document they’re cleared to see?
Turn your intranet, drives, and documents into the fastest way to find the answer — each result checked against what the requester is authorized to access before it’s returned. We build search that understands what your people mean, permission-aware, on your own content.
02 Credentials
Elastic Generative AI PartnerOur lead Elastic credential — Elastic Select Partner status held alongside it.
70+ certified engineersHands-on Elastic depth across the whole stack.
15 billion documents migratedProduction scale across client engagements.
Elastic-certified engineering depth
Elastic Certified Engineer
Elastic Certified Analyst
Elastic Certified Observability Engineer
Elastic Certified SIEM AnalystWorked directly with Elastic’s own Japan and US engineering teams on a live search migration.
Who calls usFortune 500 companiesGovernments and public sectorGlobal enterprisesBig 4
What we build in from day one
Document- & field-level securityAccess enforced inside the index itself, not filtered after the fact.
Role-based access controlModeled against your real roles and documents before rollout.
On-prem, VPC, or air-gappedDeployment options for regulated and classified environments.
03 The problem
Every search that fails is an hour of work redone — or a document seen by the wrong person.
Search is becoming a conversation inside the company too. Your team just asks, in their own words — and expects the right, permitted answer back.
Time lost
- Keyword search matches words, not meaning. Ask for “the liability cap clause in our Acme Corp master services agreement” and it returns zero results — even though the clause is sitting in a PDF three folders deep.
- The employee doesn’t file a ticket. They ask a colleague, or guess, or start over. Every failed search is an hour paid twice — once to redo the work, once for the original work no one could find.
Access risk
- A folder-tree permission model doesn’t know what a document is about — only where it sits. Sensitive files end up reachable by people who were never meant to see them.
- Every ungoverned result is an audit finding waiting to happen. The question isn’t if it surfaces — it’s who was watching when it did.
We build search that reads intent and respects permissions — so those queries return the right document, to the right person, every time.
04 The opportunity
Leapfrog the search box your team already works around.
Most companies still bolt keyword search onto a folder tree, one system at a time. That gap is your opening.
Move first, and instant, grounded answers over your own knowledge become your advantage — before it’s everyone’s baseline, and before the next audit asks why you couldn’t find it.
05 Where it applies
Not just IT — anywhere the answer is buried in a system your team already has access to.
Anywhere someone asks a plain question and the answer is locked inside a document, a ticket, or a record they’re authorized to view. Different department, same retrieval-plus-permission problem.
Ten places this shows up first
Ranked roughly by how much time — or risk — each one carries.
- Legal contract searchLegal & procurement teams. The exact clause is real and findable — but three folders deep in a PDF nobody has time to reread.
- Enterprise intranet & knowledge searchEvery employee. Policies, how-tos, and forms scattered across five systems, so people just ask a colleague instead.
- Knowledge base & support searchSupport agents. The fix already exists in a ticket from last year; finding it is slower than re-solving it.
- Engineering docs & runbook searchOn-call engineers. The fix is written down somewhere — but “somewhere” isn't searchable at 2 a.m.
- Customer 360 / entity searchSales & customer success. One customer's orders, tickets, and notes live in four systems that don't talk to each other.
- Legal & eDiscovery searchLitigation & compliance teams. Every privileged document has to be found and flagged, with chain of custody intact.
- Healthcare & clinical records searchClinicians. A patient's history is complete — just not retrievable fast enough to act on.
- Finance & audit-evidence searchFinance & internal audit. An auditor asks for evidence a control existed; someone spends a week proving it.
- HR & policy / handbook searchHR & every manager. The policy is current — employees just can't find the version that applies to them.
- Grounding an internal AI assistantIT & platform teams. A company copilot is only as good as what it can retrieve — and retrieve safely.
06 How it works
Search that understands what your team means — and only shows what they’re allowed to see.
It runs on Elastic. Elasticsearch is the retrieval engine — keyword, semantic, vector, and hybrid in one platform. Here is how a plain-language query becomes the right, permitted result.
An employee asks
“the liability cap clause in our Acme Corp master services agreement”
“the liability cap clause in our Acme Corp master services agreement”
Understand intentThe query is read for meaning — which system, which entity, what timeframe — not just matching words.
Retrieve on ElasticsearchThree signals run in a single query, then fuse:Fused with Reciprocal Rank Fusion (RRF) — neither signal dominates.
Exact termBM25 keyword match for names and terms.
SemanticELSER matches meaning, not just terms.
VectorkNN over embeddings finds close matches.
Rank & surfaceResults are ordered for relevance, recency, and source authority.
Ground & generateThe AI Answers layer composes one cited answer from what was just retrieved and permitted.
Ranked resultsThe right, permitted documents, ordered to act on.
Grounded answerOne cited, permission-checked answer. Expanded next →
The retrieval foundation, built on Elastic
Semantic search
ELSER, Elastic’s native model for retrieval, matches on meaning — so a question finds the right clause even when it never shared a keyword with it.
Vector search
kNN over dense embeddings finds close matches. Bring your own model — OpenAI, Google, Cohere, or open-source.
Hybrid search
BM25 exact-term and vector signals fused with RRF in one query — built for content that mixes formal language and plain questions.
Permission-aware retrieval
Document- and field-level security enforced inside the query itself — not filtered afterward, and not bolted on.
Built on Elastic · Elastic Select PartnerGoogle Cloud Premier Partner for Google embedding modelsDeployable on-prem, in your VPC, or air-gapped
We design and build this retrieval layer on your content — choosing where semantic, vector, hybrid, and permission filtering each earn their place.
07 AI Answers
One cited answer — not a folder tree to dig through.
An employee asks a real question and gets one specific answer built from your own documents, every source cited — not a shared drive to dig through, and not a hallucinated guess.
01
02
Grounded, with sources
It answers only from your own documents, every claim cited back to the source file and location — never from outside your content.
03
Falls back when unsure
Low confidence? It returns the ranked, permitted results instead. No bluffing — and no answer built from a document it shouldn’t have surfaced.
We build this on the same Elastic retrieval underneath — grounded, permission-checked answers, tuned on your own content.
Proof
Security modeled before rollout — not bolted on after.
Real Elastic search and document-intelligence engagements, reused here because they are the closest fit to permission-aware, internal-document search — the same governance discipline is the standard we build to.
9-nodeElasticsearch cluster with RBAC and field- and document-level security from day one — global professional-services firm
200K+documents migrated to real-time search across 4 domains — Japanese engineering conglomerate
Hardened AWSstack built, secured, and tuned for a legal document-intelligence pipeline — Supreme Court judgment summarization
Secure search
Global professional-services firm — Solr migrated to a 9-node Elasticsearch cluster on GKE, with role-based access and field- and document-level security modeled in before the first index existed.
Read the case study Document intelligenceLegal data-science company — dense Supreme Court judgments turned into short summaries and audio, on a hardened AWS stack SquareShift built and secured.
Read the case study Site search at scaleGlobal Japanese electrical-engineering conglomerate — 200K+ documents moved to real-time search across 4 domains on one crawler, working directly with Elastic's own engineers.
Read the case study09 Solution Catalog
Pick a starting point — scoped, priced, and fast.
Every engagement is fixed-scope and outcome-first. Start free to prove the value, then buy the exact improvement your documents need.
Start free: put a specialist on your hardest document-search problem
Primary — start hereBook
Document Search Readiness Assessment
Find where your team can't find what they need, how much it costs you, and where access controls are weakest.
- Free assessment
- Where you're ready, it leads straight into a one-week proof of concept.
Time1 week
CostFree
AI Answers POC
See grounded, cited, permission-checked answers on your own documents before you commit.
- A working answer demo on a slice of your real content
- Proof of value with no build commitment
Time1 week
CostFree
Senior advisory to secure, tune, or migrate — scoped and priced up front
Permissions & Governance Tuning
ACL and field-level security modeled correctly before rollout.
- Access-control model verified against real roles and documents
- Gaps found and fixed — checked, not assumed
Time2 weeks
Cost$10k
Search Performance & Scale Tuning
Fast, stable search under real load, at document-corpus scale.
- Latency and stability tuned for peak usage
- Headroom for corpus and query growth
Time2 weeks
Cost$10k
Migration & Consolidation Advisory
A plan to move off Confluence search, SharePoint search, Splunk, or another platform, with the risks mapped before you commit.
- Source-by-source risks, sequence, and effort
- Before you move anything
TimeSprint
CostOn request
Enterprise Search Build
Go from a folder tree to permission-aware, intent-aware search across your intranet and documents.
- Semantic, vector, and hybrid retrieval built on your content
- Permission filtering built into the query, not bolted on after
TimeBuild
CostOn request
11 Book a session
See it work on your own documents.
Bring one system or document set your team can’t search well — an intranet, a contract repository, a knowledge base. In a scoped working session we produce a permission-aware search prototype on your own data — not a generic demo.