enterprise AI agents
AI assistants that know your business and stay within your rules
- Pilot
- 8–12 weeks
- Starts with
- A diagnostic, typically 1–3 weeks
- Measured against
- Your own agreed baseline
The problem
Does this sound familiar?
Who it is for
Organisations that want to use large language models on their own documents and systems without losing control of their data, including where it must stay in-country or on premises.
A typical situation
Our staff lose hours looking for the right policy, contract clause or customer record, and the same questions reach the same experts every day. People are already pasting company data into public AI tools. We want the productivity, but with control over where data goes, who can see what, and how answers are checked.
Outcomes
What Enterprise AI Agents & Private AI can improve
Indicative ranges, validated in your pilot. These figures come from comparable industry programmes, not from TashIT projects. Your pilot sets a baseline with you and measures the actual change.
- Indicative
Time spent finding information
Typically 30-60% less
Basis: Retrieval-based assistants over well-maintained policy, procedure and record collections. Depends on document quality, coverage and adoption.
- Indicative
Handling time for routine requests
Typically 20-40% shorter
Basis: Agents that draft responses, fill forms and update systems with a person approving each action. Depends on how standardised the process is.
- Indicative
Routine questions reaching specialists
Typically 20-40% fewer
Basis: Internal assistants for HR, IT, compliance and operations questions. Depends on how well source documents cover the questions asked.
From data to decision
How enterprise AI agents work
Step 1: Pick the use case and the measure
We choose one high-volume task, such as policy questions from frontline staff, order and stock queries from trade customers, contract review, customer email triage or credit memo drafting, and record a baseline for time, volume and quality.
Step 2: Connect knowledge and systems
Documents from SharePoint, file shares, wikis, ticketing tools and databases are indexed with their access permissions preserved. System actions are connected through APIs.
Step 3: Build the assistant or agent
Assistants use retrieval-augmented generation and cite their sources. Agents get defined tools, step limits and approval points before anything is sent, changed or paid.
Step 4: Evaluate before release
Test sets built with your experts measure accuracy, citation correctness and behaviour when no answer exists. Red-team tests cover prompt injection and data leakage.
Step 5: Deploy with governance
Models run in a private cloud, in-country region or on-premises as required, with single sign-on, role-based access, logging and monitoring. Each assistant has a named owner, a usage policy and scheduled re-evaluation as documents and models change.
Data and integration
What we need, and what it connects to
Data we need
- The document collections in scope, with owners and access rules
- Example questions or tasks, with good answers from your experts
- APIs or databases the agent will read from or act on
- Identity and access setup, for example Microsoft Entra ID or another single sign-on provider
- Data residency, retention and regulatory requirements
- Baseline measures: request volumes, handling times and escalation rates
Integrates with
- Microsoft 365, SharePoint and Teams, or Google Workspace
- Ticketing and CRM systems (ServiceNow, Jira, Salesforce, Zoho and others)
- ERP and core business systems through their APIs
- Databases and data warehouses
- Hosted and open-weight language models, in a cloud region you approve or on your own hardware
- Single sign-on and identity providers
How the pilot works
Piloting enterprise AI agents
Duration
- 8–12 weeks
Scope
- One use case for one team, deployed to a defined user group in your environment, with an evaluation set agreed up front and governance controls in place from the first release.
Success measures
Agreed in writing before work starts, with targets set against your baseline.
- Answer accuracy and citation correctness on the agreed evaluation set
- Time saved per task or request against the baseline
- Weekly active users and repeat use in the pilot group
- No data leaving approved environments, confirmed by access logs
The pilot follows a short diagnostic and ends with a written results review. Scaling up is a separate decision, made on the measured results. How our engagements work
Where it applies
Industries and capabilities
Industries where it applies
- Financial Services & InsuranceBanks, insurers and fintechs: document AI, fraud analytics and process automation.
- HealthcareHospitals, laboratories and insurers: claims, receivables, patient flow and integration.
- Public Sector & InfrastructurePublic programmes, cities and infrastructure operators: data integrity, monitoring and service delivery.
- Energy & UtilitiesPower, gas, water and mining: losses, availability and asset performance.
- Retail & DistributionGrocery, fashion and national distributors: forecasting, allocation, shrink and route cost.
Capabilities behind this solution
- AI & Machine LearningCustom models, AI agents, computer vision, predictive analytics and private AI deployments, integrated with the systems you already run.
- Software EngineeringEnterprise applications, SaaS platforms, ERP and CRM extensions, APIs and cloud systems engineered for long-term operation.
- Business Process AutomationWorkflow automation, finance and inventory integrations, automated reporting and document processing that remove manual work.
Keep exploring
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FAQs
Questions about enterprise AI agents
Something else to ask?
Email your question with a little about your site and systems, and we will reply in plain language.
info@tashit.comCan the assistant see documents a user is not allowed to see?
It is designed so that it cannot. Document permissions are carried into the search index, and the assistant only retrieves what the signed-in user can already open. This is tested explicitly before release.
Which language model do you use?
The one that fits your data, cost and residency requirements. That can be a hosted model in a cloud region you approve, or an open-weight model on your own servers. The architecture lets you change models later without rebuilding the assistant.
How do you stop it from making things up?
Answers are grounded in retrieved documents and show their sources. When the documents do not support an answer, the assistant is designed to say so. Evaluation sets, user feedback and periodic review catch drift before it becomes a problem.
Ready to scope a pilot?
Tell us about your site or process and the data you already have. We will reply with a diagnostic plan, the measures we would propose for a pilot and an honest view of feasibility.