NeoBramPlan an AI project
    Industrial AI solution

    AI Contract Intelligence For EPC & Construction

    Extract clauses, track obligations, analyse change orders and flag risk across FIDIC, NEC, EPC and EPCM contracts - integrated with Primavera, SAP and your DMS.

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    AI contract intelligence for EPC and construction projects

    Acceptance before scale

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

    Direct answer

    NeoBram builds AI contract intelligence for EPC and construction firms - clause extraction, obligation and milestone tracking, change-order impact analysis, and risk flagging across FIDIC, NEC, EPC and EPCM contracts, integrated with Primavera P6, SAP and your document management system.

    Private deployment available

    Why evaluate it

    Contract Risk Is Hidden In 5,000 Pages.

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

    A single EPC project contract often runs to several thousand pages across the main agreement, annexes, technical specs, subcontracts and amendments. Critical obligations, change-order entitlements and risk-allocation clauses are buried where commercial and project teams can't reliably find them.

    We deploy AI contract intelligence that ingests your full contract set, extracts clauses, builds an obligation register, links obligations to Primavera milestones and SAP cost codes, analyses change orders against the baseline, and flags risk in plain language for commercial, project and legal teams.

    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.

    Clause extraction & obligation register

    Auto-extract clauses, obligations, deliverables, payment terms, LDs, warranties and conditions precedent from main contract, annexes and subcontracts into a structured obligation register.

    • Clause-level classification per FIDIC / NEC / EPC taxonomy
    • Obligation register with owner, deadline, dependency
    • Linked to Primavera milestones and SAP cost codes
    • Multilingual contracts (EN, FR, ES, AR, ZH)

    Change-order analysis

    Compare proposed change orders against the baseline contract, flag scope-creep vs. true variation, estimate cost and schedule impact, and surface entitlement and notice-period clauses.

    • Baseline vs change-order redline analysis
    • Scope-creep vs variation classification
    • Cost and schedule impact estimation
    • Entitlement and notice-period clause surfacing

    Risk flagging in plain language

    Surface unusual or onerous clauses (caps on LDs, indemnities, IP, termination for convenience, currency exposure) in plain English for commercial, project and legal review.

    • Onerous-clause detection vs market benchmark
    • Liquidated damages, caps and indemnity highlight
    • Termination, IP and currency risk surfacing
    • Side-by-side comparison with playbook clauses

    Project & commercial dashboards

    Live dashboards for project, commercial and legal teams showing upcoming obligations, change-order pipeline, risk heatmap and claim exposure across the portfolio.

    • Upcoming-obligations 30 / 60 / 90 day view
    • Change-order pipeline and cumulative impact
    • Project risk heatmap with drill-down
    • Portfolio-level claim and exposure rollup

    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 ingestion

    • Unstructured.io / LayoutLMv3

      Layout-aware PDF parsing

    • AWS Textract / Azure Doc Intel

      OCR and table extraction

    • LlamaParse

      Long-form contract chunking

    Models

    • Claude 3.5 Sonnet (200K context)

      Long-document reasoning

    • GPT-4o

      Clause classification

    • Llama 3.3 70B (on-prem option)

      Sovereign deployment

    • Custom fine-tuned clause classifiers

      FIDIC / NEC / EPC specific

    Knowledge layer

    • Qdrant / pgvector

      Contract clause vector store

    • Neo4j

      Obligation and party graph

    • BGE-M3 embeddings

      Multilingual contracts

    Integration

    • Primavera P6 / Oracle PPM

      Milestone linking

    • SAP S/4HANA Project System

      Cost-code mapping

    • Aconex / Procore / SharePoint

      DMS source

    • Power BI / Tableau

      Risk and obligation dashboards

    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 do you handle confidential and privileged contract content?+
    Contracts are processed inside your environment - private cloud or on-premise - with no data sent to public LLM APIs unless your security policy permits. For sovereign deployments we use Llama 3.3 70B on your GPUs. For cloud deployments we use Azure OpenAI or AWS Bedrock with no-training, no-retention configurations. Privileged content is tagged and access-controlled at the document level.
    Will the AI replace our commercial and legal teams?+
    No. The system is decision-support. It surfaces clauses, drafts analyses and flags risk so commercial managers, contract administrators and counsel spend their time on judgement, not on hunting through PDFs. Every AI output is cited back to the source clause for human review. Final positions on claims, change orders and risk are made by your team.
    How accurate is clause extraction on real EPC contracts?+
    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.
    Can the system handle multiple contracts and subcontracts together?+
    Yes - this is the core value. The system ingests the main contract, annexes, subcontracts, side letters and amendments together, links obligations across them, and flags conflicts (e.g. main contract LD caps vs subcontract pass-through). The knowledge graph makes it possible to answer 'who owes what to whom by when' across the full document set.
    How does this integrate with Primavera and SAP?+
    Obligations and milestones extracted from contracts are mapped to Primavera P6 activities and SAP S/4HANA PS cost codes via a configurable mapping layer. Slippage in Primavera surfaces affected contractual obligations and any LD exposure. Change orders push as draft variation entries into SAP PS for commercial approval.
    What's a typical pilot and rollout?+
    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.

    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