Implementation Service
Knowledge Management Solutions Implementation
Deploy AI-powered semantic search, GenAI / RAG, and engineering-content platforms so engineers find answers in minutes instead of hours across millions of technical documents. Our primary delivery is on Accuris Goldfire and Goldfire Chat — the engineering-domain platform where content is semantically pre-indexed and every answer is source-traceable. We also integrate OpenText Documentum into regulated engineering workflows, and run vendor-neutral evaluation across the broader landscape (Sinequa, Coveo, Glean, Microsoft Copilot / Azure AI Search, Lucidworks, Mindbreeze, IntraFind, AlphaSense) against your corpus, security model, and TCO.
Published author on Cognitive AI for engineering knowledge augmentation (SAE International)
Hands-on experience building semantic search and knowledge extraction products for enterprise engineering organizations
Combines AI/NLP expertise with deep engineering domain knowledge across 4 industries
Practical taxonomy design informed by how engineers actually search, not how librarians organize
Implementation Timeline
Knowledge Audit
2-3 weeksSource inventory, taxonomy workshop, information architecture design, and metadata schema definition.
Configuration
4-6 weeksConnector setup, indexing pipeline configuration, search profile creation, and relevancy algorithm tuning.
Integration
3-4 weeksDMS integration, authentication and SSO setup, access control configuration, and cross-system linking.
Training & Launch
2-3 weeksUser training workshops, champion user onboarding, adoption playbook delivery, and feedback collection.
Optimization
OngoingUsage analytics monitoring, relevancy tuning based on search behavior, source expansion, and continuous improvement.
Our Approach
Knowledge Source Inventory & Taxonomy Workshop
Catalog all knowledge sources -- documents, standards, patents, procedures, lessons learned -- and conduct workshops to define the taxonomy and metadata schema.
Information Architecture & Search Strategy Design
Design the information architecture, define search profiles by engineering discipline, and establish relevancy ranking rules aligned with engineering workflows.
Semantic Search Configuration & Connector Deployment
Configure the semantic search platform, deploy connectors to all knowledge sources, and set up indexing schedules and access control policies.
Search Profile Customization & Relevancy Tuning
Create discipline-specific search profiles, tune relevancy algorithms using engineering-specific heuristics, and validate results with subject matter experts.
User Training, Launch & Ongoing Optimization
Deliver hands-on training workshops, launch the platform with champion users, and establish feedback loops for continuous relevancy and coverage improvement.
What We Deliver
Knowledge source inventory and taxonomy
Information architecture and search strategy document
Configured semantic search platform with custom connectors
Discipline-specific search profiles and relevancy tuning
User training materials and adoption playbook
Who It's For
Engineering organizations with large volumes of unstructured technical documents
R&D teams needing to search across patents, standards, and internal technical literature
Knowledge management leaders tasked with capturing institutional expertise
Chief engineers concerned about knowledge loss from workforce turnover
Expected Outcomes
Dramatic Search Time Reduction
Sub-minute search across millions of technical documents, down from typical 30-45 minute manual searches through filing systems and shared drives.
Discipline-Specific Search Profiles
Discipline-specific search profiles tailored to each engineering function, ensuring that mechanical, electrical, process, and structural engineers each see the most relevant results first.
Connected Knowledge Bases
Connected knowledge bases spanning internal documents, standards, patents, and published research -- breaking down silos between information sources.
Reduced Duplicated Research
Measurable reduction in duplicated research and reinvention of existing solutions, preserving institutional knowledge even as experienced engineers retire.
Illustrative Projects
Enterprise Knowledge Platform
Deploying semantic search across 2M+ technical documents for a global EPC firm, including integration with document management systems and standards libraries
R&D Knowledge Base
Connecting internal design documents, patents, and standards into a unified searchable knowledge base with discipline-specific search profiles
Standards-Integrated Search
Configuring semantic search to surface relevant standards alongside internal technical procedures, linked to active programs and design requirements
Goldfire Chat RAG Rollout
Deployed Goldfire Chat with hybrid retrieval, reranking, and citation grounding across engineering procedures, standards, and lessons-learned for a Tier-1 OEM — wired to clearance-aware access controls.
GraphRAG-Enabled Knowledge Platform
Built a GraphRAG layer over a 3M+ document corpus for a GCC NOC — linking assets, standards, suppliers, and lessons through a domain knowledge graph so agentic retrieval can answer multi-hop engineering questions.
50-Year Well-File Goldfire Knowledge Corpus
Built a Goldfire-powered semantic knowledge corpus for a GCC national oil company covering 50 years of well-file data, OpenText-scanned image archives (OCR-recovered drilling reports, mud logs, well completion records), internal engineering standards, and the international standards portfolio (API, ISO, NACE). Engineers now ask natural-language questions across the full subsurface, drilling, and completions history in seconds — replacing weeks of manual filing-cabinet retrieval. Measurable ROI on well-planning cycle time, lessons-learned reuse, and avoided rework.
Engagement Models
Discovery & Knowledge Audit
Inventory of knowledge sources, taxonomy workshop, and information architecture recommendations with a deployment roadmap.
2-3 weeks
Standard Deployment
Full deployment of semantic search across a single site or business unit, including connector setup, relevancy tuning, and user training.
3-4 months
Enterprise Multi-Site
Enterprise-wide deployment across multiple sites and business units with unified taxonomy, federated search, and centralized governance.
6-12 months
RAG Architecture Pilot
Time-boxed pilot to stand up a production-grade RAG architecture against a focused engineering corpus — hybrid retrieval, domain-tuned embeddings, reranking, citation grounding, and a measured evaluation harness — to de-risk the full enterprise build.
8-12 weeks
Vendor Landscape
Our primary platform
Accuris Goldfire — engineering knowledge, pre-indexed
Goldfire is where our delivery depth lives. It is the only enterprise platform that ships with engineering content already understood — standards, patents, technical literature, and your internal corpus — indexed semantically rather than as keywords.
Generic enterprise search and modern workplace assistants treat your engineering corpus like any other body of text. Goldfire was built for engineers: more than two decades of natural-language processing tuned to engineering syntax and terminology across oil & gas, aerospace & defense, manufacturing, and energy. Every answer is traceable to the source passage, so design decisions stay defensible — which matters when the answer drives a calculation, a safety case, or a regulatory submission.
Outcome ranges published by Accuris (accuristech.com). Engineers spend up to 42% of their working time searching across an average of 13 disconnected sources — Goldfire collapses that into one semantically-aware surface.
What it does
Semantic engineering search, not keyword retrieval
Goldfire understands what an engineer is asking — requirements, properties, components, technical concepts — and surfaces the precise passage rather than a list of documents to triage.
Pre-indexed engineering content
Ingests internal documents, designs, and reports alongside trusted external engineering content (standards, patents, technical literature) without manual tagging or rule-building.
Source-traceable answers
Every answer is backed by verified source material — preserving rationale and evidence so decisions remain explainable, repeatable, and audit-defensible.
Federated, multi-server reach
Recent releases (25.1.1) extend Goldfire with federated search across multiple servers, so a single query reaches a global knowledge base while respecting per-region access controls.
Flexible deployment posture
SaaS, customer-hosted, or fully on-premise — the right answer for export-controlled aerospace & defense, sovereign oil & gas, and regulated manufacturing environments.
Built-in connectors to engineering systems
First-class integrations into SharePoint, document management systems, and engineering content sources mean shorter time-to-first-answer and lower integration cost than a generic-search build-out.
Goldfire Chat — the engineering GenAI assistant
Goldfire Chat layers generative AI on top of Goldfire's semantic engine so engineers get conversational, cited answers grounded in the organisation's own data — not the open internet, and not a generic Copilot.
Grounded generation, not generic LLM
Answers are constrained to indexed organisational knowledge through Goldfire's semantic retrieval — preventing the hallucination patterns that make generic LLMs unsafe for engineering decisions.
Trained on engineering data
Twenty-plus years of engineering NLP behind every response. Understands specialised syntax and terminology across oil & gas, aerospace & defense, and manufacturing.
Citation-first by design
Summarises dense test reports, answers compliance queries, and traces every claim back to the originating standard, procedure, or technical document — a hard prerequisite for regulated work.
API-first deployment
The Goldfire Chat API integrates with existing engineering tools and workflows — embed it in your PLM, your standards portal, your QMS, or stand it up as a dedicated assistant.
We evaluate every vendor in the table below. When the answer is engineering content — standards, design rationale, lessons-learned, regulatory submissions — Goldfire wins on engineering depth, on time-to-first-answer (because the content is already indexed), and on the defensibility that regulated industries require. That is where our delivery, consulting, and support is concentrated.
| Category | Vendors we evaluate |
|---|---|
| Engineering-domain semantic platforms | Accuris Goldfire, Goldfire Chat, Sinequa, Coveo, Mindbreeze, IntraFind Primary delivery (Goldfire / Goldfire Chat) |
| Engineering content & document platforms | OpenText Documentum, OpenText Content Cloud, OpenText Magellan Direct experience (Documentum) |
| Modern workplace search & GenAI assistants | Glean, Microsoft Copilot, Azure AI Search, Lucidworks Fusion / Springboard Vendor-neutral evaluation |
| Domain-vertical insight platforms | AlphaSense (market & external research), Accuris Engineering Workbench Vendor-neutral evaluation |
| RAG & retrieval infrastructure | Vector stores (pgvector, OpenSearch k-NN, Pinecone, Weaviate), reranker models (Cohere Rerank, BGE), embedding models (OpenAI, Voyage, BGE) Vendor-neutral evaluation |
| LLM and model orchestration | Anthropic Claude, OpenAI GPT-class, Azure OpenAI, AWS Bedrock, model routing and policy frameworks Vendor-neutral evaluation |
RAG Architecture Patterns
- 1
Hybrid retrieval (BM25 + dense)
Combine lexical (BM25) and dense vector retrieval with rank fusion so engineering queries that depend on exact part numbers, standards codes, and acronyms surface alongside semantically similar passages.
- 2
GraphRAG
Layer a knowledge graph over the corpus — assets, systems, standards, suppliers, lessons — so retrieval can traverse relationships (e.g., "which standards apply to this assembly under API 510?") rather than relying on flat similarity.
- 3
Agentic RAG
Decompose multi-step engineering questions into planned sub-queries (retrieval, calculation, comparison, citation), executed by tool-using agents with guardrails — for design-rationale lookups, failure-mode investigations, and procedure synthesis.
- 4
Domain-tuned embeddings
Fine-tune or adapt embedding models on engineering-domain corpora (standards, procedures, lessons-learned) so retrieval understands oilfield, aerospace, and process-industry vocabulary that generic embeddings dilute.
- 5
Cross-encoder reranking
Re-rank top-k candidates with a cross-encoder reranker (Cohere Rerank, BGE-reranker) to push the genuinely relevant passages into the top three — where answer quality lives.
- 6
Citation grounding
Force the generator to cite the exact source passages used in every answer — including page numbers, standard sections, and document IDs — so engineers can verify before they act.
- 7
Long-context + RAG hybrid
Use long-context models for synthesis after retrieval narrows the candidate set — combining the precision of RAG with the reasoning capacity of large-context models for complex multi-document questions.
- 8
Access-controlled context
Apply identity-aware filtering at retrieval time — not just at the UI — so RAG responses respect document classification, export-control flags, project NDAs, and clearance levels.
Capabilities
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