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Developing Targeted GenAI Knowledge Assistants
Solution
Bring Your Organizational Knowledge to Life—With a GenAI Assistant.
Targeted knowledge assistants help teams get accurate answers from internal content—policies, procedures, product documentation, tickets, and more—without hunting across systems. Our approach focuses on answer trustworthiness, security, and adoption so the assistant performs reliably in real workflows, not just impressive demos.
The Challenge
Most knowledge assistants struggle when information is scattered, content quality is uneven, and trust breaks the first time an answer is wrong or can’t be traced back to a source. Common constraints include:
- Knowledge is fragmented across wikis, drives, ticketing systems, and intranets
- Content quality varies (outdated, duplicated, conflicting “sources of truth”)
- Access control is complex (role-based permissions, sensitive content)
- Finding the right content is inconsistent (poor structure, weak metadata, missing filters, no freshness strategy)
- Answers lack traceability (no citations, unclear provenance, inconsistent formatting)
- Quality isn’t measurable (no test set, no automated checks, no regression gates)
You need a structured approach that prepares your knowledge, finds reliably relevant content, and delivers answers users can trust—every time.
Our Solution
We design and build targeted knowledge assistants that deliver high-precision answers with citations—grounded in your approved sources and governed for enterprise use. The integrated solution includes
- Defining the assistant blueprint: target users, top questions, boundaries, “source-of-truth” rules, and success metrics.
- Preparing your knowledge for reliable answers: select sources, implement ingestion and structuring, improve metadata, and define ownership and freshness rules.
- Implementing high-quality enterprise search: optimize how content is indexed and found (filters, relevance tuning, and ranking) and ensure citations/provenance are captured.
- Building governance and safeguards: role-based access, sensitive-data handling, auditing/logging, policy constraints, and escalation paths.
- Hardening, launching, and improving: evaluation datasets and automated checks, edge-case testing, monitoring, rollout enablement, and an iteration roadmap.
Targeted Benefits
- Faster time-to-answer for common questions by reducing manual search and “tribal knowledge” dependency
- Higher trust and fewer mistakes through citations, source transparency, and clear escalation for uncertain cases
- Improved operational consistency by guiding teams to the same approved policies, procedures, and playbooks
- Reduced risk and stronger governance with role-based access, logging/auditing, and sensitive-data safeguards
- A foundation for continuous improvement with measurable quality checks, monitoring, and a roadmap to expand coverage
Solution Essentials
Format
Remote / on-site / hybrid (build sprints + stakeholder checkpoints)
Duration
Typically 3–6 weeks for an initial targeted assistant (varies by sources, permissions, and quality needs)
Engagement Model
Pilot build (fixed scope) or sprint-based delivery (iterative)