A retrieval-augmented LLM slashes engineers' proposal information-processing time by roughly 90% and produced no expert-detected hallucinations in domain tests, promising large per-cycle cost savings — though findings rest on a small, sector-specific experiment.

Main Finding

A Retrieval-Augmented Generation (RAG) LLM assistant deployed for information management in commercial proposals for electrical-generation plant retrofit projects reduced information-processing lead time from a manual range of 2–30 minutes to a predictable 1–2 minutes (≈90% cycle-time reduction), produced a zero-detected-hallucination rate under the experimental protocol, and yielded a projected operating-cost saving of roughly 90% per cycle. The study concludes that RAG architectures can be a reliable, efficient decision‑support layer for pre‑project engineering workflows when combined with careful retrieval, citation, and governance design.

Key Points

• Experimental result: median/manual cycle-time reduced ~90% (manual 2–30 min → RAG 1–2 min).
• Hallucination control: system instructed to cite sources and mark inferences; expert panel detected zero hallucinations in the evaluated trials.
• Economic projection: operating-cost savings per information-search cycle projected near 90%.
• Evaluation framework: triangulated through data-driven, risk‑based, and value‑driven project management lenses.
• Two reusable reference prompts were formalized (supplier-quotation comparison; proposal-version traceability) producing structured outputs (normalized tables, executive notes, rankings, risk/traceability flags).
• Contribution: first empirical end‑to‑end RAG deployment and controlled comparison in engineering commercial-proposal workflows (electrical sector retrofit context).

Data & Methods

• Research design: mixed-methods experimental comparison (qualitative proof-of-concept + quantitative controlled tests).
• Unit of analysis: single information‑search tasks in pre‑project phase — (a) supplier‑quotation comparison, (b) proposal‑version traceability.
• Technical stack:
  • Presentation: React 19 + Material‑UI v7.
  • Typing: TypeScript.
  • LLM/reasoning: Claude Opus 4.6 via API.
  • Retrieval: standard RAG pipeline — chunking/indexing of documents into overlapping segments, embeddings stored in vector index, top‑k retrieval, LLM conditioned on retrieved chunks; model instructed to cite sources inline.
• Corpus: 50 historical commercial‑proposal records for seven buyers in the electrical generation sector; 8 upgrade categories; prices ≈ USD 6,000–50,000.
• Participants: 3 experienced proposal engineers (min. 4 years retrofit experience); bilingual (ES/EN).
• Experimental protocol:
  • Proof of concept: two extraction prompts to verify corpus traversal.
  • Quantitative stage: each user ran 15 randomized tasks per scenario (supplier comparison and version traceability) manually and then with RAG.
  • Measured variables: execution time (minutes), hallucination presence (binary, validated by a three‑member expert panel), successful completion.
  • Anti‑hallucination constraints embedded in prompts: no invention of fields, mark inferences, cite sources.
• Outputs produced by assistant:
  • Supplier comparison: normalized comparative table, 250‑word executive note, supplier ranking (strengths/weaknesses), risk flags (high/med/low).
  • Version traceability: comparative version table, 300‑word evolution note, ranking by internal success criteria (margin, residual risk, lead time, compliance), traceability risk flags.
• Limitations noted by authors: small corpus and user sample, domain specificity (electrical retrofit sector), single LLM/provider in the stack, controlled experimental conditions may understate real‑world integration/edge‑case risks.

Implications for AI Economics

• Direct productivity and cost effects:
  • Near‑term per‑task cost reduction and cycle‑time compression (~90%) imply high short‑run ROI for proposal teams, especially in environments with many repetitive, knowledge‑intensive searches.
  • Predictable, shorter cycle times reduce response variance—improves bidding throughput and enables more simultaneous bids per engineering headcount.
• Value capture & competitive dynamics:
  • Firms that invest in curated corpora and RAG infrastructure can compound gains across repeated proposals; regulated, repeatable markets (like large utilities/IPPs) amplify returns to such investments.
  • Faster, higher‑quality responses can raise win rates in competitive procurements, increasing revenue potential beyond pure cost savings.
• Labor and task composition:
  • RAG reduces time on low‑value search/classification tasks, enabling reallocation of engineers toward higher‑value activities (client interaction, bid strategy, engineering design), though it may also reduce demand for routine proposal labor.
  • Upskilling and governance roles (data curation, RAG prompt engineering, review) become more important.
• Risk, governance, and implementation costs:
  • Study shows hallucination risk can be materially reduced with retrieval + citation + expert review, but real‑world deployments require ongoing maintenance: corpus curation, index updates, access controls, compliance checking.
  • Dependence on a commercial LLM provider (Claude Opus in the study) introduces vendor, privacy, and cost‑structure considerations; total cost of ownership must account for embedding/index infrastructure, compute/API spend, and review overhead.
• Scaling and externalities:
  • Aggregate gains scale nonlinearly with volume of repetitive tasks; high‑volume proposers benefit most.
  • Market-level labor displacement could be moderate since the task set is narrow; however, reallocation pressures and wage/skill premium effects are likely for senior engineers.
• Policy and managerial recommendations:
  • Prioritize investment in curated, structured corpora and retrieval infrastructure to secure high factual grounding.
  • Embed human‑in‑the‑loop review for compliance‑critical claims and maintain traceability logs for audits.
  • Model ROI including reduced cycle time, increased bid throughput, potential conversion uplift, and ongoing governance costs.
• Research implications for AI economics:
  • Need for larger-scale field studies measuring downstream effects (conversion rates, contract margins, labor reallocation) and long‑run dynamics (maintenance costs, model drift, regulatory risk).
  • Comparative cost–benefit analyses across sectors with differing document repeatability and regulation to identify where RAG yields the highest economic returns.

Summary judgment: In regulated, repeatable, knowledge‑intensive B2B settings (like electrical retrofit proposals), RAG‑augmented LLMs can deliver large, credible productivity and cost gains if deployed with disciplined retrieval, citation, and governance—making them economically attractive investments for firms with sufficient proposal volume and the ability to maintain curated corpora.