We use ChatGPT or Copilot today. Why another layer?
It augments rather than replaces. A PoC measures whether company context improves utilization of your current or planned LLM workflow.
No Prompt. Input Context.
Enterprise AI for role-based reporting grounded in internal work context.
Prepare reviewed work context through on-premise servers, terminology and relationship maps, permissions, audit, and governed execution flows.
No model replacement · review-ready work context · PoC baseline
A new candidate is reviewed, prepared as usable work context, and then attached to the existing LLM prompt.
01 / Problem
The LLM can answer.
It just does not know your company context.
Repeated reporting, mismatched terminology, and missing approval history vary by organization. Measure the operating hypothesis first in a PoC.
Repeated reporting
The same report lives across email, docs, and chat, forcing owners to find sources and the latest version again.
Time baseline
Terms and ownership
When teams use the same words differently, LLM answers drift. First align terminology and ownership.
Term seed
Approval and audit history
When revision reasons and accepted evidence are not captured, the next request starts from scratch.
Approval flow
02 / PoC diagnosis scope
Three checks for the PoC.
Agree on the baseline first.
This is not an outcome claim. First measure where ChatGPT, Claude, or Copilot workflows stall.
Once the three baselines are agreed, the PoC can decide data scope, terminology-map seed, and organization-console review flow.
Discuss PoC scopeBaseline before outcome claims
01
LLM utilization baseline
Tool, team, and workflow usage
02
Repeated context cost
Terminology, approval, and history re-entry
03
Work-context scope
Connected sources, evidence links, review inbox
03 / Solution
Prepare repeated company context before the question
Traditional LLM workflows make users explain company context every time. ONESHIM prepares approved work context before the question.
LLM by itself
1Write background for each question
2Paste company terms and approvals
3Retry due to missing context
4Manually clean up verified output
Humans refill context each time
ONESHIM augmentation
1Collect context candidates from approved sources
2Map company terms and relationships
3Prepare evidence-linked work context
4Connect reviewed packets to LLM workflows
No Prompt. Input Context.
04 / Product model
Collect, structure, and review context.
01
Client
Open-source desktop client
Collect approved source candidates
Source audit→
02
Server
Terminology and knowledge graph engine
Structure terminology and relationships
Context layer→
03
Console
Enterprise operations console
Operate review inboxes, permissions, and audit
Ops consoleDetailed product screens continue in the platform tour.
05 / Buyer questions
Four checks before adoption
Can sensitive internal data stay off external clouds?
SaaS, Demo, and On-prem paths are reviewed separately. The internal-network boundary and Client source-audit scope are agreed before PoC data is connected.
Doesn't mapping terminology and relationships take months?
A PoC separates initial seeding, tuning, and KPI measurement. ONESHIM starts from documents, logs, and DB context.
How are ROI and payback calculated?
Start with utilization, repeated explanation time, and LLM scope and cost, then recalibrate with approved PoC measurements or synthetic logs.
06 / Next step
Choose the right entry point
C-level / Partner
PoC inquiry
LLM utilization baseline and adoption conditions
Ask about PoCIT decision-maker
Platform tour
FuturePac operations console
Start tourDeveloper / LLM user
Client source audit
Apache 2.0 client · Standalone boundary
Audit source