SERVICE · AI COPILOT
AI Copilot Deployment
A workplace assistant people actually use. Wired into your internal knowledge, tools, and permissions, with every answer sourced and verifiable, living right inside Slack, the browser, and the systems you already work in.
DEFINITION · WHAT IS AN AI COPILOT
What is an AI Copilot?
An enterprise AI Copilot is an AI work assistant wired into your company's internal knowledge. Built on a RAG (retrieval-augmented generation) architecture, it answers questions grounded in your own documents, ERP/CRM, and databases, inherits your existing permission model in full so users only see what they're already allowed to see, attaches a clickable, verifiable source to every answer, and lives inside the workflows your employees already use (Slack, a browser extension, your internal systems) instead of being yet another tool to open. What separates it from a general chatbot: it is grounded in your private knowledge, obeys your permissions, and its answers can be checked.
Grounded in private knowledge
Answers come from your real documents and systems, not the model's memory or the public web; RAG retrieval lets every response be backed by your internal data.
Inherits existing permissions
Retrieval applies the user's own access rights, so they only see what they were already allowed to see. Asking the Copilot never leaks across departments.
Sourced and auditable
Every answer carries clickable source links and cited passages, and the backend keeps a full log of queries and citations for compliance and internal audit.
Lives in existing workflows
Embedded in Slack, the browser, and internal systems, it shows up where employees already work. No extra tab to open, no new interface to learn.
COMPARISON · AI COPILOT vs A GENERAL CHATBOT
How is an enterprise AI Copilot different from a general chatbot?
A general chatbot is great at conversation over public knowledge, but it doesn't know anything about your company's internals and has no idea who is allowed to see what. An enterprise Copilot solves exactly those two problems: it grounds answers in your private knowledge and permissions, and makes every response verifiable and governable. The difference isn't whose model is smarter. It's whether it's wired into your real systems.
General chatbot / consumer ChatGPT
Enterprise AI Copilot (delivered by Tenten)
Knowledge source / grounding
Relies on model memory and the public web; doesn't know your internal documents or data
RAG-connected to your knowledge base, ERP/CRM and databases; answers grounded in private knowledge
Permission control
No concept of permissions; it's all-or-nothing access
Fully inherits your permission model; users only see what they're already allowed to see
Source traceability / audit
Answers have no provenance; can't be verified, hard to hold accountable, high hallucination risk
Every answer carries clickable sources and citations, with a query-and-citation log for audit
Where it shows up (workflow)
A standalone chat window; employees must open a tab and switch context
Embedded in Slack, the browser and internal systems; lives in the workflows employees already use
Tool and action integration
Conversation only; can't check an order, pull a report, or start an approval
Securely calls tools to act: check status, file a ticket, kick off an approval
Data governance / deployment
Data sent to a public service, potentially logged or used for training
Deployed in your VPC or designated cloud; data never leaves, never enters model training
Continuous evaluation / quality
Fixed quality; can't be tuned to your use case, and can't be measured
Eval sets and human labeling quantify hit rate and hallucination rate, iterated week over week
Move your Copilot from demo to daily work
Most companies that roll out an AI assistant get stuck in the same place. The demo dazzles. After launch, nobody uses it. Answers hallucinate, so people stop trusting it. Teams won't feed it internal data for fear of leaks. And it sits in yet another tab nobody opens. The system gathers dust and returns nothing. The model was never the problem. It just isn't connected to your knowledge, doesn't respect your permissions, and doesn't show up where people already work.
We treat the Copilot as an engineering problem, not a procurement line item. Our Forward Deployed Engineers embed on-site and work next to your IT, legal, and business teams to connect the assistant to your knowledge base, ERP/CRM, document systems, and existing permission model. Every answer carries a clickable source link from a RAG architecture, so when something looks off you can trace it back to the exact document and passage. Your data stays inside your VPC or designated cloud (Azure, AWS) and is never used for training.
Launch is the starting line. We stand up a continuous evaluation loop (real questions, human labeling, automated scoring) to quantify hit rate and hallucination rate, then tune it weekly so quality climbs after go-live instead of decaying. A first usable assistant ships in roughly 5 to 6 weeks, so you prove the result in one high-value scenario before rolling it sideways.
Capabilities
01
Wired into your knowledge and permissions
A RAG architecture connects Confluence, SharePoint, Google Drive, Notion, ERP/CRM, and databases, and inherits your existing permission model in full. Users only ever see what they were already cleared to see. Nothing leaks across departments.
02
Every answer carries its source
Responses come with clickable source links and cited passages, so anyone can open the original in one click. The backend logs every question and citation, which makes compliance and internal audit straightforward and keeps hallucination risk in check.
03
Lives inside your existing workflow
It embeds into Slack, a browser extension, and your internal systems. No new tab to open, no new interface to learn. The Copilot shows up exactly where people already do their work.
04
Connects to tools and takes action
Beyond answering questions, it can check order status, pull a report, open a ticket, or start an approval through secure, permission-scoped tool calls that move the Copilot from finding information to getting things done.
05
Continuous evaluation and quality loop
We build eval datasets, human labeling, and automated scoring to quantify hit rate and hallucination rate, then tune prompts, retrieval, and guardrails weekly against real usage. Quality keeps climbing after launch.
06
Data governance your security team signs off on
Deployed in your VPC or designated cloud, data never leaves your environment and never enters model training. We support SSO, audit logs, PII redaction, and SOC 2 / GDPR requirements, built to pass your legal and security review.
Use cases
Knowledge Assistant for Support Teams
Support reps ask 'what's the policy for this return case' right in Slack or the ticketing system, and the Copilot retrieves from product manuals, SOPs, and past tickets to give a sourced, consistent answer. New hires ramp faster and fewer cases escalate.
Sales and Pre-Sales Knowledge on Demand
Reps pull product specs, competitive comparisons, pricing, and prior deal examples before a meeting. By tying together CRM and the internal wiki, pre-sales stops chasing colleagues for information and waiting on other departments.
Shop-Floor and Engineering Lookups
Field engineers query MES fault codes, equipment maintenance manuals, and quality standards. Connected to document systems and machine data, the Copilot makes senior technicians' know-how searchable and transferable.
Compliance and Risk Lookups
In finance and regulated industries, compliance staff query KYC/AML policies, internal rules, and the latest directives. The Copilot returns sourced answers and keeps an audit trail, so every judgment call rests on evidence you can pull up.
HR and Internal IT Self-Service
Employees self-serve on leave policies, expense workflows, benefits, and IT FAQs. Drawing from HR policy and the IT knowledge base, the Copilot offloads repetitive internal questions from human help desks.
Supply Chain and Logistics Status
Operations staff use the Copilot to query shipment status, inventory, and order exceptions across TMS/WMS, turning information scattered across multiple systems into something you can get to in a single question.
Delivery cadence
WEEK 1
Scope the Scenario, Map the Data
FDEs embed with your team to pick the first high-ROI scenario, map knowledge sources, the permission model, and security boundaries, and define success metrics and how we'll evaluate them.
WEEK 2–3
Connect Knowledge and Build RAG
We complete data connections, permission inheritance, and RAG retrieval, wire in source citations and guardrails, and produce a first assistant ready for internal testing.
WEEK 4–5
Embed in the Workflow and Pilot
We embed the Copilot in Slack, the browser, or internal systems, invite seed users to test it for real, and tune retrieval, prompts, and hallucination guardrails against the eval data.
WEEK 5–6
Launch and Keep Evaluating
We open it to the target team, establish a continuous evaluation loop and a weekly iteration cadence, quantify adoption and ROI, and plan the sideways rollout to the next scenario.
5–6 weeks
To first launch
100%
Answers sourced, every one verifiable
VPC
Data stays in your environment
FAQ
How do you handle AI hallucination?
We use a RAG architecture so answers are grounded in your actual documents rather than generated from thin air, and every answer carries a clickable source for one-click verification. We also add guardrails so that when there's no reliable basis to retrieve, the Copilot says 'I couldn't find it' instead of making something up. After launch, we quantify and steadily drive down the hallucination rate against an eval dataset, week over week.
Will our internal data leak or get used to train models?
No. The Copilot is deployed inside your VPC or designated cloud (Azure, AWS), so data never leaves your environment and never enters any model training. We support SSO, audit logs, and PII redaction, and we can meet SOC 2 / GDPR and similar compliance requirements, so your security and legal teams can sign off.
Won't this end up unused like the last tool we bought?
That's the risk we care about most, and it sits at the core of the design. We embed the Copilot into the Slack, browser, and internal systems people already use, so they never have to open a new tool or learn a new interface. Add the trust that clickable sources create and the quality gains from weekly evaluation, and adoption actually sticks.
How are permissions controlled? Can regular staff see executive data?
The Copilot fully inherits your existing permission model and applies the individual user's access rights at retrieval time, so they only ever see what they were already cleared to see. Cross-department or sensitive data won't leak just because someone asked the Copilot, and every retrieval and citation is logged for audit.
How fast do we see ROI, and how do we prove it works?
A first usable assistant ships in roughly 5 to 6 weeks, and we focus on one high-value scenario so you get a result you can take to the board this quarter: lower support response time, shorter new-hire ramp, fewer internal questions. We set the success metrics with you in week one and let the evaluation data make the case, not gut feel.
How is FDE on-site delivery different from a typical consulting rollout?
FDE means real engineers embed in your team to write the code, connect the systems, and tune the quality. No slide deck, no leaving. We work shoulder to shoulder with your IT, legal, and business teams on the hardest part, data access and permissions, get the Copilot to a launch-ready state, and leave behind an evaluation mechanism your team can keep running.
KNOWLEDGE BASE · FURTHER READING
The AI Copilot rollout knowledge base
We've organized the key knowledge for an enterprise Copilot rollout here, from Tenten AI's first-hand delivery perspective to the authoritative external sources on RAG and getting enterprise AI into production, so you can judge how to make the assistant actually adopted and ROI-positive.
Tenten AI whitepapers & perspectives
INSIGHTS · LATEST
The latest on enterprise AI & delivery
First-hand observations and methodology from Tenten AI's delivery floor.
AI workflows,
built into your operations
We deploy forward — FDE and FDM — to build the AI agents and workflows your team runs on. Live in weeks, not quarters.