Unitforce Technologies
Consulting Pvt Ltd
An assistant inside the tool people already have open, grounded in what your company actually knows.
Personal AI assistants embedded in the tools your people already open — drafting, summarizing, fetching, generating. We ground them in your own knowledge so they answer your way, and stay quiet when they lack evidence.
The reason most internal AI assistants go unused is not the model — it is the extra tab. A copilot earns its keep when it appears in the application where the work is already happening: the CRM record, the ticket, the document, the admin console.
So we build copilots as embedded surfaces in your own products and internal tools, with the context of the screen already loaded. The user does not restate who the customer is or which policy applies; the copilot can already see it.
A copilot is only trusted if it answers from your material. We ground them in your knowledge base, policies and product documentation — the same retrieval approach behind our RAG work — so answers reflect your organisation rather than a general impression of your industry.
Behaviour is shaped per role, because a support agent, an account manager and a finance approver need different defaults, different tone and different data. What each role may retrieve is enforced at the retrieval layer, not asked for politely in a prompt.
Drafting, summarising, fetching and generating are the tasks copilots do best, and all four end with a person deciding. We design for that: output is proposed, editable and attributable, never silently committed on someone's behalf.
Where a copilot lacks evidence, it says so. Staying quiet is a deliberate behaviour, and it is what keeps people willing to rely on it for the answers it does give.
The honest measure of a copilot is whether the work it touches gets done faster and to the same standard — not how many people opened it in week one. We instrument acceptance: how often a draft is used as-is, edited, or discarded, and which tasks account for each.
That signal is what drives the next iteration. A prompt or retrieval change is evaluated against the same task set every time, so improvement is a number the team can see rather than an impression somebody formed in a demo.
An assistant embedded in an application your team already uses, which drafts, summarises, retrieves and generates in the context of what is on screen. Unlike a standalone chatbot, it starts with the record, ticket or document already in view.
Yes. A copilot is normally built as a surface inside your current product or internal tool, using its APIs for context and actions, so people do not have to move to a separate application to use it.
Permissions are enforced where retrieval happens, so a user's role determines which documents and records can be fetched at all. The model never gets the chance to decline access to something it was already handed.
It says so. Copilots we build are instructed to abstain rather than improvise when grounding evidence is missing, and to point the user to a person or source instead.
Agents that do the work, not just describe it — wired into the systems your team already uses.
Generative AI that answers from your facts, cites where they came from, and says nothing when it has no evidence.
Whole processes automated end to end, with the exceptions routed to people instead of hidden from them.
Fraud flagged as it happens and credit scored at the point of decision — with reasons an auditor will accept.
The unglamorous half of AI — versioning, serving, monitoring and rollback — owned by us so your team doesn't carry it.