Knowledge copilots
Policy, product, and operational Q&A with hierarchical access, employees see only corpora their role entitles them to.
Generative AI
LLM applications built for enterprise knowledge work.
LLM applications, RAG, copilots, fine-tuning, and knowledge systems with evaluation harnesses, guardrails, and private deployment in your cloud estate.
Grounded
Retrieval with source citations
Scoped
Role-aware knowledge boundaries
Tested
Eval suites before each release
Deployable
Private cloud or VPC options
Dual view
Enterprise gen AI vs. consumer chat
Consumer patterns we reject
Open web retrieval on proprietary questions
No attribution for generated claims
Single shared prompt with no tenant isolation
Patterns we implement
- Corpus scoped to approved internal sources
- Inline citations and refusal when evidence is thin
- Per-tenant or per-BU index partitions
- Red-team and jailbreak testing in CI pipelines
Capabilities
Application archetypes
Each archetype ships with threat modeling appropriate to its audience and data sensitivity.
Authoring assistants
Draft generation for proposals, clinical notes, or support replies with template guardrails and mandatory review queues.
Conversational service layers
Customer-facing assistants that escalate to humans with full transcript, retrieved sources, and suggested resolution paths.
Code copilots & AI SDLC
Context-aware helpers bound to approved repos and schemas, review, test, and change workflows with secrets scanning and output filtering. Not an unbounded coding chatbot.
Capabilities
What we put in the release path
Model choice is one decision. Production quality is a system of contracts, evals, and gates.
Evaluation harnesses
Golden sets, faithfulness checks, and regression suites in CI, so a prompt or index change cannot ship on vibes.
Red-team and jailbreak testing
Prompt-injection, data-exfil, and policy-bypass cases run before customer-facing or regulated releases.
Prompt & retrieval contracts
Versioned prompts, chunking, and re-rankers with rollback, the same discipline as application code.
Fine-tunes when RAG plateaus
Adapters and supervised fine-tunes only after retrieval and prompting are exhausted, with data governance and a rollback plan.
Perspective
Quality is a systems problem, not a model pick
Leaders often anchor generative AI discussions on model selection. In production, answer quality depends on chunking strategy, metadata richness, re-rankers, prompt contracts, and feedback loops from real users.
InheritX engineers the full stack: ingestion pipelines that respect document lifecycle, retrieval graphs that mirror how experts search, and evaluation harnesses that score faithfulness, not just fluency.
We align with legal and compliance early on retention policies, training boundaries, and logging so generative features launch with documented controls, not retrofitted disclaimers.
Fit
Matching architecture to risk profile
Internal productivity on non-sensitive docs
Shared RAG service with SSO and standard DLP scanning
Regulated content with external visibility
Human-in-the-loop publish, immutable citation store, extended logging
Low-latency customer chat at scale
Cached retrieval, model tiering, and graceful handoff to live agents
Multi-language enterprise knowledge
Language-aware indexing with cross-lingual retrieval validation
FAQ
Generative AI FAQ
When retrieval and prompting plateau, we evaluate supervised fine-tuning or adapters, always with clear data governance and rollback plans.
Grounding, citation requirements, confidence thresholds, and automated faithfulness checks against retrieved passages.
Yes. We integrate your existing enterprise agreements through a gateway that centralizes keys, routing, and usage telemetry.
Yes, scoped to approved repositories, identity, and change policy. We treat AI SDLC as a governed assistant in your delivery path, not an open internet coder.
Continue
Continue exploring
Next step
Map this capability to your mandate.
A focused strategy conversation, constraints, systems, and what production readiness looks like for your organization.