Extracting contract insights with PwC’s AI‑driven annotation on AWS

Contracts hold rights, obligations, and financial hooks that shape business outcomes. PwC’s AI‑driven annotation (AIDA) makes that trapped value findable and auditable by combining OCR, template‑based extraction, large language models (LLMs) and retrieval‑augmented generation (RAG) on cloud infrastructure.

Reported results from pilots are striking: one major film and TV studio saw about a 90% reduction in time spent on rights research. That kind of efficiency flips contract review from a manual choke point into an automated, searchable business asset.

AIDA converts unstructured agreements into structured, searchable insights so teams can find and reuse critical contract information faster.

Why executives should care

• Speed: Faster answers to legal, procurement and compliance questions—reducing downstream delays and legal bottlenecks.
• Consistency: Template‑driven extraction delivers repeatable outputs across thousands of documents, reducing human variability.
• Auditability: RAG-based answers link back to source clauses, creating verifiable trails for auditors and lawyers.
• Actionability: Structured outputs feed CLMs, ERPs and CRMs to trigger renewals, obligations and financial entries.

Business case snapshot

Start with high‑value, high‑volume contract types: IP rights, renewal/termination clauses, indemnities, licensing and payment terms. A typical pilot can demonstrate results quickly because these clause categories are well‑defined and materially important.

Example ROI sketch (simplified):

• Average review time per contract prior to AI: 2 hours
• Contracts reviewed per year: 10,000
• Reviewer fully loaded hourly cost: $120
• Reported reduction in time: 50–90% depending on contract type and maturity of templates

At 70% time savings, annual labor savings = 10,000 × 2 hrs × $120 × 0.7 = $1.68M. Subtract operating cost of the AI system (compute, model calls, storage, maintenance) to estimate net benefit. Sensitivity to percentage savings and reviewer cost makes a short pilot essential to firm estimates.

How AIDA works — plain language

Think of AIDA as a layered pipeline:

1. Ingest and OCR: PDFs and scans are converted into searchable text.
2. Template extraction: Rule‑based templates capture predictable fields (dates, parties, monetary terms).
3. Semantic indexing: Contracts are converted into vector representations (embeddings) so similar language and concepts can be found even when phrased differently.
4. RAG and LLM interpretation: Semantic search retrieves relevant passages; an LLM synthesizes answers grounded in those passages and returns citations to source text.
5. Human review & integration: Reviewers validate outputs, then structured data flows into CLMs, ERPs or downstream workflows.

Put simply: templates give repeatable structure, embeddings let the system find related clauses, and RAG ensures answers are tied back to the original contract text so they’re explainable.

Pilot checklist and 90‑day playbook

Run a focused pilot to measure time savings, accuracy and integration effort. A simple 12‑week plan:

• Weeks 0–2 — Scope & intake: Pick 1–3 contract categories and define success metrics (time saved, extraction accuracy, number of exceptions).
• Weeks 3–5 — Data prep & OCR tuning: Gather a representative sample set and tune OCR settings for scanned documents.
• Weeks 6–8 — Template creation & model setup: Build extraction templates and configure semantic indexing and RAG parameters.
• Weeks 9–10 — Human‑in‑the‑loop testing: Route outputs to reviewers, capture feedback, and refine templates and thresholds.
• Weeks 11–12 — Integration & measurement: Push validated outputs to the chosen CLM/ERP and measure against KPIs; prepare scaling plan.

Key KPIs to track:

• Extraction accuracy (per field)
• Precision and recall for clause identification
• Average human review time per contract
• Percentage of contracts requiring manual correction
• Throughput (documents processed per hour/day)
• User satisfaction and time to decision

Top implementation risks and mitigations

• Poor OCR quality: Mitigation — invest in image preprocessing and use high‑quality OCR engines; sample different vendors if needed.
• Template underfit or overfit: Mitigation — iterate templates with human feedback and track field‑level accuracy.
• Hallucinations from LLMs: Mitigation — use RAG so answers are grounded in source text and require human sign‑off for binding decisions.
• Data residency and compliance gaps: Mitigation — enforce project‑level controls, encryption, and choose regions that meet regulatory requirements.
• Cost overruns (model calls, indexing): Mitigation — monitor cost drivers, batch processing where possible, and set budget thresholds for model usage.

Governance and trust: what to set up first

• Define a legal validation process for AI outputs and establish thresholds for when human approval is required.
• Decide data residency and retention policies up front; ensure encryption at rest and in transit.
• Assign ownership: who maintains templates, who audits accuracy, and who manages integrations into CLM/ERP.
• Log and version all extracted outputs and provide clickable citations back to source clauses for auditability.

Technical deep dive (for architects and engineers)

AIDA on AWS uses established enterprise building blocks to balance scalability, security and observability. Core components include:

• Storage: Amazon S3 for raw files and OCR outputs; Amazon RDS for structured extraction results and metadata.
• Processing: Containerized OCR and extraction tasks on Amazon ECS / Fargate, coordinated with Amazon SQS for asynchronous jobs.
• Semantic search: Embeddings produced by models hosted on Amazon Bedrock are indexed in Amazon OpenSearch Serverless to enable fast vector and metadata queries.
• LLM & RAG: Amazon Bedrock hosts foundation models, knowledge bases and provides guardrails; retrieved passages feed an LLM to generate grounded answers with citations.
• Security & identity: Edge protection via AWS WAF and load balancers; authentication via Amazon Cognito integrated with enterprise IdPs (Okta, Microsoft Entra); IAM and KMS enforce least‑privilege and encryption.
• Integrations: AWS Lambda, EventBridge and SQS handle downstream delivery to CLM, ERP or CRM systems, with human‑in‑the‑loop validation gates.
• Observability & CI/CD: CloudWatch, AWS X‑Ray, CodeBuild and CodePipeline for monitoring, tracing and deployment pipelines; CloudTrail for audit logs.
• Visualization: Amazon QuickSight dashboards track throughput, OCR accuracy and extraction bottlenecks.

Caveats and engineering tradeoffs:

• Embeddings and semantic indexes are model‑dependent. If multi‑cloud portability matters, plan for exportable formats and abstraction layers for retrieval logic.
• Bedrock model calls are a primary cost driver. Use caching, batching, and hybrid approaches (rule‑first, model‑second) to reduce volume.
• Guardrails help reduce unsafe outputs but are not foolproof. Maintain human checkpoints for legally binding decisions.

Real‑world mini case study

A media company needed fast answers about distribution rights across thousands of legacy contracts. Using template extraction for common metadata and RAG for clause nuance, the team cut rights‑research time by roughly 90% for targeted queries. Human reviewers were retained for edge cases, while structured outputs automated renewal notifications to finance and licensing systems.

Common executive questions — quick answers

• How accurate is contract AI in practice?
  

  Extraction accuracy typically ranges with maturity: roughly 80–95% for well‑defined fields after template tuning and OCR optimization. Clause identification accuracy varies more depending on language variability.

• Can AI outputs be used for legal decisions?
  

  AI can reliably surface candidate clauses and structured metadata, but legal sign‑off and human validation are recommended before taking binding actions.

• How long does a pilot take?
  

  A focused pilot on a narrow set of contract types can run in 8–12 weeks and deliver measurable KPIs.

• What are the main cost drivers?
  

  Model calls (LLMs/embeddings), storage and indexing, OCR processing, and ongoing human review are primary cost components.

• How do we avoid vendor lock‑in?
  

  Design for portability: keep templates exportable, store embeddings and metadata in open formats, and abstract retrieval logic so models can be swapped with minimal rework.

• What are common failure modes?
  

  Poorly scanned documents, inconsistent clause language, and insufficient human feedback loops are the usual culprits; address them early in the pilot.

• Which teams should be involved?
  

  Legal ops, procurement, IT/cloud engineers, security/compliance, and a small group of power users who will validate outputs and iterate templates.

Next steps for leaders

• Pick 1–2 high‑value contract types for a 90‑day pilot.
• Define success metrics up front: time saved, extraction accuracy, and integration endpoints.
• Assemble a cross‑functional team: legal ops, procurement, IT, and a reviewer pool for human‑in‑the‑loop validation.
• Plan for governance: data residency, audit trails, and legal sign‑off thresholds.

PwC’s approach with AIDA shows how contract AI can be operationalized: template logic for repeatability, semantic search for nuance, and RAG for explainable answers. When combined with strong governance and a focused pilot, AIDA‑style systems move contract intelligence from experiments into everyday business value.