The same questions interrupt the same experts
Three senior people answer the same twenty questions every week in chat. Their real work waits, and the answers vanish into scrollback.
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AI knowledge
AI Knowledge Assistants for Internal Teams
Answers from your own documents, with the source attached.
We build internal assistants that answer questions from your policies, contracts, tickets, and manuals, respect who may see what, cite their sources, and say so when they do not know.
Start with a 30-day assessment Internal assistantCITED ANSWERS
What is our refund policy for annual plans after 30 days?
Billing Policy v4.2§3.1 Approvals MatrixRow 14 Support RunbookCancellations
Refunded pro rata for unused full months, less onboarding 1. Refunds above $2,000 need finance approval 2.
Permissions respectedSays “I don’t know”
The operating problem
Your company already wrote the answer. Nobody can find it.
Policies, contracts, runbooks, tickets, and product manuals hold most of what people ask each other every day. A useful assistant retrieves the right passage, answers in plain language, shows where the answer came from, respects permissions, and admits when the documents do not cover the question.
30 daysFixed-scope assessment
How it works in practice
An answer you can check, or an honest refusal
Illustrative internal assistant for an operations team
What is our policy on refunds for annual plans cancelled after 30 days?
Annual plans cancelled after 30 days are refunded pro rata for the unused full months, less any onboarding fee. Refunds above $2,000 need finance approval. 1 2
Can we offer the same terms to resellers?
I could not find reseller refund terms in the documents I can access. The Billing Policy covers direct customers only. The owner of that policy is the Finance lead.
Sources used · permissions checked
1 Billing Policy v4.2Section 3.1 · Annual plans All staff
2 Finance Approvals MatrixRow 14 · Refunds Finance, Support leads
3 Support RunbookCancellations Support
The third source was retrieved but not cited because the asker is not in Support. Permissions come from your document systems.
AI knowledge assistants by the numbersIllustrative figures from representative engagements
20questions asked every weekThe same ones, to the same three experts
100%of answers cite a sourceDocument, section, and version
0answers from outside your documentsThe assistant refuses when the sources are silent
SSOpermissions respected per userPeople see only what they may already read
Signals it is time
If these situations feel familiar, this is worth assessing.
The answer exists but nobody can find it
The policy is in a PDF on a shared drive, the exception is in an email, and the current version is in someone’s head. Search returns forty files.
A generic chatbot invents company policy
A public chatbot answers confidently from general knowledge. It sounds right, it is not your policy, and nobody can check where it came from.
Side by side
The same work, done two ways.
Every row is a step your team handles today. The right column is what the workflow does after the build, with people kept where judgment is needed.
StepHow it happens todayAfter automation
Finding an answer
Ask a senior colleague in chat
Ask the assistant, get the passage
Trust
Depends who answered
Every answer cites its source
Permissions
Whoever has the file
Enforced per user
Unknowns
A confident guess
An honest refusal and an owner to ask
Documentation gaps
Invisible
Logged and reported monthly
Illustrative scenarioA 120-person logistics company
Operations stopped asking the same three people and started checking the source.
“Situation
Rate rules, customs procedures, and customer-specific terms lived in PDFs, a wiki, and years of email. New staff took months to become independent, and three senior people spent hours a day answering chat questions.
What changed
We indexed the policy documents, wiki, and customer term sheets with their access rules, and deployed an assistant in the team chat. Every answer links to the passage it used. Questions it cannot answer are logged for the document owners.
Result
An illustrative example of a typical engagement, not a named client case study.Routine questions answered in seconds with a citation
Senior staff interruptions dropped sharply
A monthly list of documentation gaps to fix
What we build
A working system—not an automation slide deck.
The engagement is sized around one useful operational outcome. Your team receives the implementation, operating context, and visibility needed to own it.
Knowledge source inventory
Which documents and systems hold the answers, who owns them, how current they are, and who may read them.
Retrieval and access design
Indexing, permissions, citation format, and refusal behavior designed around how your team actually asks.
Assistant with evaluation set
A deployed assistant in the tools your team uses, tested against real questions with known answers.
Works with your stack
AI knowledge assistants built around the tools you already run.
We connect to what is in place through APIs, exports, and databases. Nothing here requires a platform change, and tools not listed are usually reachable too.
SPSharePoint and OneDriveSources
GDGoogle DriveSources
WikiConfluence and NotionSources
HDHelpdesk ticketsSources
AIClaude and OpenAIModels
VecVector searchRetrieval
SlSlack and TeamsWhere people ask
SSOSingle sign-onPermissions
+Your other toolsAssessed in the first call
Delivery process
Diagnose. Design. Build. Measure.
Each stage has a clear decision and output, so the project remains connected to the business problem.
Find where the answers live
We map the sources, their owners, freshness, and who may read each one, and collect real questions with known answers.
Retrieval with permissions and citations
Content is indexed with its access rules. Answers cite the passage they used, and the assistant refuses when the sources are silent.
Measure answer quality
The question set runs on every change. Unanswered questions become a list of documents worth writing.
Is this the right first step?
Good fit when. Not yet when.
Good fit when
The same questions are asked again and again
The answers exist in documents, even if scattered
Wrong answers have a real cost, so citations matter
Someone owns the documents and will fix gaps
Not yet when
The knowledge exists only in people’s heads
Documents are badly out of date and nobody owns them
The goal is a public-facing bot before an internal one works
The 30-day assessment says whichWhat good looks like
Less handling. Fewer errors. Faster answers.
Common questions are answered in seconds with the source attached.
People see only what their permissions allow.
The assistant says it does not know instead of guessing.
Gaps in documentation become visible and fixable.
Questions about ai knowledge assistants
Answered before you book.
Three questions we hear most often about this service. The rest is answered in the assessment.
Will it make things up?+
It answers only from the retrieved passages and shows them. When the sources do not cover the question it says so. That refusal behavior is tested in the evaluation set.
Can people see documents they should not?+
No. Retrieval respects the permissions already set in your document systems, so an answer never draws on a file the asker cannot open.
Does our data train a public model?+
No. We use API and enterprise terms that exclude training on your data, and the deployment options are covered in the assessment.
How this engagement starts30day assessment
Thirty days inside the process and the tools around it. You finish knowing what to automate, with what, in which order, and how long it will take.
Start a 30-day assessment Fixed scope · read-only access · written findings you keepA 30‑day assessment before anything gets built.
Where your process stands
How the work really flows today: volumes, handoffs, waits, error rates, and the automations that already exist.
What you can do about it
Which steps to automate, which need an AI step, which should stay human, and which to leave alone.
Which tools will help
n8n, Zapier, Make, agents, or custom code: what fits your stack, team, and budget, and what would be hype.
What to improve in the process
Rules, ownership, and data fixes that should come before or alongside any automation.
How to work with legacy systems
If older software is in the way: how to connect to it, wrap it, or upgrade it so automation is possible.
How long it takes, and the roadmap
A sequenced build plan with durations, costs to expect, and the measures that prove it worked.
Week by week
What happens in each of the four weeks.
Kickoff and scope
We walk you through our in-house analysis system, which scans your code, repositories, and databases, and agree exactly what the assessment will cover and what you will receive.
- Scope, systems, and people agreed
- What you will get: architecture, workflows, recommendations, timeline, risks
- A go or no-go decision before any access is given
NDA, access, and the scan
Once you are ready to proceed, we sign an NDA and you grant read-only access to the necessary repositories and databases. Then our system runs.
- NDA signed, read-only access granted
- Data lineage traced across code and databases
- Workflows and dependencies identified
Verify and weigh the options
Senior engineers verify what the system found in working sessions with your team, and capture what is not written down anywhere.
- Findings confirmed or corrected with your people
- Options, tools, and process fixes compared
- Legacy upgrade paths tested against your constraints
Roadmap and readout
You receive the written findings and we walk the decision makers through them.
- Overall architecture and workflow documentation
- Recommendations and risks, in priority order
- Timeline and roadmap for the work
Who is behind AICO Services
Built and delivered by Trobus Technologies.
A Maryland-based engineering company. AICO Services is how we package our AI automation, modernization, and assessment work. Our engineers have delivered for organizations including these.
Organizations represented in the broader Trobus Technologies delivery history. Engagement scope and team role vary; reference details are shared where authorized.
Delivery recordRepresentative production engineering record. Details available where authorized.
4systems in production4 yrscontinuous operation990+automated tests1,361commitsCompany qualifications
Credentials maintained by Trobus Technologies, LLC
MBE
Minority Business Enterprise
MDOT
Maryland DOT certified
SBA WOSB
Woman-Owned Small Business
WOSB
Women-Owned Small Business
E-Verify
Participating employer
Free PDF · 2 pages
Get the 30‑day assessment specification.
The exact deliverables, the week-by-week schedule, where it applies, and the engagement terms. Share it with the people who need to approve the work.
- Six written deliverables
- Week-by-week schedule
- Engagement terms
A promise before the proposal
We will tell you when not to automate it. The assessment says so in writing.
30-day assessment
Start a 30-day assessment What could ai knowledge assistants change for your team?
The 30-day assessment tells you whether it is a good candidate, which tools fit, and how long the build will take.