NeoBramPlan an AI project
    Industrial AI solution

    Engineering Document Search AI Find the Answer, Not the Folder

    Ask natural-language questions across P&IDs, drawings, specs, standards and reports. Get cited answers in seconds, with full access control.

    Plan the use case Explore ROI tools
    AI search for engineering documents and drawings

    Acceptance before scale

    Baseline, representative test set, failure conditions and a named human owner are defined before production approval.

    Direct answer

    NeoBram delivers AI document search for engineering and EPC firms - a RAG assistant that answers natural-language questions across P&IDs, drawings, specs, standards and project documentation with verifiable citations and full access control inheritance from your CDE.

    Private deployment available

    Why evaluate it

    Engineers Spend Hours Hunting for Documents.

    The decision is whether this capability improves a defined workflow safely not whether an AI demo looks impressive.

    Modern retrieval-augmented generation can change this. An AI assistant indexes your engineering documents - drawings, P&IDs, specifications, codes, standards, vendor data, reports - and answers natural-language questions with cited sources.

    We build engineering search that respects your access control, runs on your infrastructure and gives every answer a verifiable trail back to the source document, page and section.

    Uncited universal benchmarks from the legacy page were withheld. Define the baseline and acceptance threshold from customer evidence or a reviewable primary source.

    Candidate capabilities

    What a production solution may need to do.

    Each capability is validated against representative data and the customer's workflow. Product or model names describe possible components, not partnerships, certifications or guaranteed compatibility.

    Natural-language engineering search

    Ask questions across all your engineering documents - drawings, P&IDs, specs, vendor data, project correspondence - in plain English.

    • Hybrid retrieval across semantic + keyword
    • Drawing and P&ID-aware (symbol, tag, equipment)
    • Cross-document reasoning when needed
    • Multi-lingual support for global projects

    Cited, verifiable answers

    Every answer cites the source document, page, section and version - so engineers can verify quickly without trusting the model blindly.

    • Inline citations with one-click source jump
    • Document version awareness
    • Confidence scoring per answer
    • 'I don't know' fallback when evidence is thin

    Workflow integrations

    Available in MS Teams, Outlook, Aconex / ProjectWise, browser and mobile - so engineers stay in their workflow.

    • MS Teams / Slack bot
    • Outlook compose assist
    • Browser plug-in for any web CDE
    • Mobile app for field use

    Access control & auditability

    Inherits document-level permissions from your CDE. Every query and response logged for audit, with PII / sensitive data controls.

    • Per-document ACL inheritance
    • SSO via Azure AD / Okta
    • Query and answer audit log
    • PII / sensitive data redaction

    Architecture context

    Select components after the boundary and test are clear.

    The list is a design vocabulary. Final selection depends on licences, data location, latency, security, existing systems and customer approval.

    Document sources

    • Aconex / Bentley ProjectWise

      Engineering CDE

    • Autodesk Construction Cloud

      BIM & drawings

    • SharePoint / OneDrive

      General docs

    • Standards libraries

      ASME, API, ISO, NEC, IEC

    Document understanding

    • Document AI (Azure Document Intelligence, AWS Textract)

      Layout + tables

    • Drawing / P&ID parsing

      Symbol & tag recognition

    • OCR + structure

      Scanned legacy docs

    Retrieval

    • Vector DB (Qdrant, Weaviate, pgvector)

      Semantic search

    • BM25 hybrid retrieval

      Keyword + semantic

    • Re-ranker (Cohere, Voyage)

      Precision boost

    Generation & guardrails

    • Private LLM endpoint

      Azure OpenAI / Bedrock / on-prem

    • Citation enforcement

      Every claim grounded

    • Access-control middleware

      ACL inheritance from CDE

    Planning ranges

    A standard path from decision to operation.

    01

    Discovery and qualification

    1-2 weeks

    Named workflow, owner, baseline, risks and go/no-go questions.

    02

    Readiness assessment

    2-4 weeks

    Data, integration, security, value and operating-readiness findings.

    03

    Technical proof of value

    4-6 weeks

    A bounded test on representative data with documented limitations.

    04

    Production pilot

    8-12 weeks

    One controlled workflow, integrated and evaluated with real users.

    05

    Enterprise or multi-site rollout

    3-6+ months

    Phased scale-out, monitoring, support and change management.

    06

    AI capability or CoE programme

    3-6+ months

    Governance, delivery methods, reusable assets and team enablement.

    These are planning ranges, not guarantees. Readiness, validation, hardware, integration, access and change management affect the schedule.

    Production safeguards

    Private deployment is one control, not the whole control system.

    Data boundary

    • Select offline, edge, on-premises or private-cloud deployment from the real operating constraints.
    • Document data flows, storage, deletion, backups and support access before production.
    • The customer approves every interface and any permitted external connection.

    Model and application controls

    • Evaluate representative cases, uncertainty and harmful failure modes before use.
    • Use suitable access control, input handling and output guardrails for the selected risk.
    • Treat grounding and citations as testable behaviours, not as a promise of perfect answers.

    Traceability and operation

    • Define identity, roles, logs, monitoring, updates, backup and incident handling.
    • Keep the evidence needed to investigate outputs and reproduce important decisions.
    • Assign a named business and technical owner for production operation.

    Human authority

    • Domain, quality, safety and regulatory owners retain decision authority.
    • Compliance depends on the implemented system and the customer's validated controls.
    • Escalation and safe fallback are part of the acceptance criteria.

    Limitations to plan for

    Performance can change with data quality, equipment, process, product mix, documents, users or operating conditions. Third-party model and software licences still apply. AI output does not replace the responsible engineer, operator, quality owner, safety professional, legal adviser or regulator.

    Buyer questions

    Direct answers before you plan a pilot.

    How does this stay aware of who can see what?+
    Access control is enforced at retrieval time, not just at the UI. We integrate with your CDE's ACL model so the AI can only retrieve from documents the asking user is entitled to see. If they shouldn't see a doc, it can't appear as a citation. This is the same model that protects search in SharePoint or Aconex.
    Does it work on legacy scanned drawings?+
    Yes, with OCR + structure recognition. Older as-built drawings, scanned reports and image-based PDFs are processed through document AI to extract searchable text, tables and (where possible) drawing tags and symbols.
    How accurate are answers - especially on technical content?+
    This legacy draft included a quantitative benchmark that has not been published with a reviewable source or customer evidence. NeoBram now treats it as an open validation question and defines the baseline, test method, acceptance threshold and limitations during discovery.
    Will the model leak our IP to OpenAI or Anthropic?+
    No. We deploy via private endpoints (Azure OpenAI, AWS Bedrock) with no-training contracts, or self-host an open-weight model (Llama 3.1, Mistral) on your GPUs. Your documents and queries never enter a public model and are not used for training.
    How does it handle drawings and P&IDs specifically?+
    We use drawing-aware document AI that extracts symbols, equipment tags, line numbers and revision blocks. Engineers can ask 'what line connects pump P-1023A to the separator on PID-1100-001?' and get an answer with the drawing reference and a snippet of the relevant view.
    Deployment timeline?+
    A focused production pilot is commonly planned over 8 12 weeks after scope, representative data, owners and acceptance tests are ready. Discovery or a technical proof of value may be shorter; integration, validation, hardware, site access and change management can extend the plan. This is a planning range, not a delivery guarantee.

    Bring one workflow

    Define the evidence, boundary and acceptance test together.

    Your team supplies process authority. NeoBram supplies AI architecture, engineering, evaluation and operating handover.

    Plan the first project