Useful automation
Focus AI on repeatable work where speed, consistency and human oversight can be measured.
Generative AI
Enterprise Search
Turn documents, tools, and tribal knowledge scattered across the business into one secure search that answers with sources—built around your permissions, not a generic connector list.
Secure answers across documents, tools and data.
- Human-in-the-loop
- controls
- Evidence-led
- evaluation
- Secure
- integration
01
Quick answer
What is Enterprise Search?
Enterprise search lets employees ask a question in natural language and get an answer grounded in the organisation's own documents, wikis, tools, and data, with sources cited—rather than manually searching separate systems one at a time.
- RAG-based search across documents, tools & data
- Permissions enforced at the source, not bolted on
- Source citations for every answer returned
- Fits where off-the-shelf search SaaS can't
Business outcomes
Built for when off-the-shelf search can't see your data.
Integr8e approaches enterprise search as a business capability—not an isolated technical task. Priorities stay connected to the users, operations and results behind the work.
Grounded answers
Connect models to approved business knowledge so responses are relevant and traceable.
Responsible adoption
Design permissions, evaluation and review controls around real operational risk.
Production readiness
Build for observability, cost control, model change and dependable integration.
What we deliver
Enterprise search services from data audit to live retrieval.
One connected team covers the decisions and delivery work needed to move from uncertainty to a dependable outcome.
Use-case strategy
Prioritise opportunities by business value, data readiness, risk and implementation effort.
Retrieval & knowledge
Prepare governed information and retrieval flows that keep answers grounded.
Agentic workflows
Connect models, tools and approval steps to complete useful multi-stage work.
AI product engineering
Create intuitive interfaces and robust services around model capabilities.
Evaluation & guardrails
Measure answer quality, safety, latency and cost before and after release.
AI operations
Monitor usage, failures, model behaviour and emerging opportunities over time.
Is this the right fit?
Best suited for IT, knowledge, and operations leaders whose teams can't find answers across documents, wikis, and internal tools, or whose data sensitivity rules out an off-the-shelf search SaaS.
Not every challenge needs the same team or solution. We start by testing the business case, current constraints and fastest credible route to value—then recommend a scope that fits the evidence.
Delivery architecture
A controlled path from data audit to a permissioned search experience.
The plan adapts to your context, while short feedback loops and visible milestones keep the engagement controlled.
- 01
Frame
Define the task, user, acceptable output, risk boundaries and measurable value.
- 02
Prepare
Assess data, permissions, integrations and the evaluation set required for confidence.
- 03
Prototype
Test the workflow quickly with representative inputs and real user feedback.
- 04
Productionise
Add guardrails, observability, security and reliable system integrations.
- 05
Evaluate & evolve
Track quality and cost continuously as models, data and needs change.
Technology context
Tools chosen around the problem—not the trend.
We select platforms against security, scale, team fit, integration needs and the full cost of ownership.
- OpenAI
- Python
- AI Agents
- Node.js
- PostgreSQL
- MongoDB
- Redis
- Docker
- AWS
- Azure
Frequently asked questions
Answers about enterprise search.
Need an answer specific to your environment? Share the context and our team will help you identify a practical next step.
What is enterprise search?
Enterprise search lets employees ask a question in natural language and get an answer grounded in the organisation's own documents, wikis, tools, and data, with sources cited—rather than manually searching separate systems one at a time.
How is a custom enterprise search build different from a tool like Glean or Coveo?
Off-the-shelf platforms cover common connectors and permission models quickly but are constrained by their own architecture and licensing. A custom build is worth considering when data sensitivity, unusual sources, permission logic, or integration requirements fall outside what a configurable platform supports well.
How does enterprise search stay grounded in our real documents instead of guessing?
Approved content is indexed and retrieved through a retrieval-augmented generation pipeline, so the model answers from retrieved passages rather than its general training data. Retrieval quality, source citation, and fallback behaviour are evaluated before launch.
How are permissions enforced so employees only see what they're allowed to?
Search results respect existing access controls by checking source-system permissions at query time or mirroring them in the index, so an employee never sees content through search that they couldn't already access directly.
Which systems can enterprise search index and connect to?
Sources can include document stores, wikis, ticketing systems, CRM and ERP data, cloud drives, and internal APIs. Connector scope, refresh frequency, and the source of truth for each system are agreed during discovery.
How do you measure whether enterprise search is actually working?
We track answer accuracy, source relevance, latency, and adoption, alongside sampled review of ambiguous or sensitive queries. Evaluation continues after launch using real employee queries, not just a one-time test set.
How much does an enterprise search project cost?
Cost depends on the number of data sources, the complexity of permission logic, and index volume. A focused search over one or two systems costs less than an organisation-wide deployment across many connectors. We scope the sources first and provide a fixed estimate rather than a generic day rate.
How long does it take to launch enterprise search?
A focused deployment across one or two data sources typically reaches a production-ready release in eight to twelve weeks, including evaluation. Broader deployments with many connectors or complex permission models take longer. Timing is confirmed after auditing the data sources involved.
Start with the outcome
Let's make your enterprise search initiative concrete.
Tell us what needs to change, who it affects and where the current approach falls short. We'll help shape a sensible next step.
hello@integr8e.com