Agentic Unconference 2026 - September 19, Chennai
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Our AI copilot development services build assistants that read your systems, draft the work, and cite sources inside the tools your teams already use. Senior engineers take them to production with evaluation and access controls, so pilots stop stalling on trust.
AI copilot development services cover designing, building, and running an AI assistant that works alongside employees inside their existing software. A copilot retrieves company data, drafts or recommends an action, and leaves the final decision with a person. The work spans retrieval, model selection, tool integration, security, evaluation, and ongoing tuning.
✓ Your experts spend hours hunting for answers. Policies, contracts, and past tickets often sit across SharePoint, Confluence, and shared drives. A retrieval-grounded copilot returns the answer with its source in seconds, and the hours saved per role are easy to measure.
✓ The work needs judgment but follows a pattern. Claims triage, contract review, and support replies repeat the same steps with small variations. The copilot drafts, a person approves, and your experts spend their time on the cases that are actually hard.
✓ Your users work inside your own product or line-of-business app. Microsoft 365 Copilot cannot see your custom CRM, your claims platform, or the SaaS product your customers log into. A custom copilot sits where those users already are.
✓ You operate under audit or regulation. In banking, insurance, and healthcare, every answer needs a source, a permission check, and a log. We design those controls with our security and compliance team from the first sprint, not after a review fails.
> Your people mainly need help in Outlook, Word, and Teams. License Microsoft 365 Copilot first and extend it with Copilot Studio if a gap appears. A custom build for email summaries is money spent on a solved problem.
> The process is fully rule-based. If every input maps to a known output, workflow automation or RPA is cheaper, faster, and easier to audit. An LLM adds cost and variance where you need neither.
> You want the system to finish tasks with no human in the loop. That is an agent, not a copilot, and it needs different guardrails, testing, and approval design. Look at agentic AI development instead, or launch a copilot and grant autonomy once it has earned trust.
We start from one workflow and the people who own it, then build a copilot that answers with citations and drafts the next step. Teams still shaping the use case often begin with generative AI consulting before any code.
Much of your most useful data sits in systems built long before LLMs. We expose SAP, Oracle, and older line-of-business records through clean APIs so a copilot can read them safely, often as the first step of wider application modernization.
We deploy inside your own cloud tenant or VPC, so prompts and documents never leave your boundary. Infrastructure is code, models sit behind private endpoints, and capacity scales with real usage rather than a guess.
A copilot that can only talk is half a product. We connect it to the systems where work happens, with scoped permissions per tool, so it can open a ticket or update a record once a person approves.
Before launch, we score answers against a test set your experts approve. After launch, tracing, drift checks, and prompt versioning run through the same MLOps and LLMOps pipeline, so quality is measured instead of assumed.
Sometimes the right copilot is an extension, not a new app. We build Copilot Studio agents, Microsoft 365 Copilot extensions, and in-product assistants that inherit the sign-in and permissions your users already have.
24%
reduction in manual support responses from automated bot integration.
Entrans rebuilt a regulatory SaaS platform on WordPress and integrated an AI-powered compliance chatbot that handles routine regulatory inquiries from the fintechs and banks it serves.
80%
Reduction in Manual Reconciliation Effort," freeing finance teams "to attend only to flagged discrepancies.
LLM functions on AWS Bedrock extract invoice fields from PDFs and scans, match them against purchase orders and goods receipt notes, and route only flagged discrepancies to people.
3X
Accelerated prior authorization processing by 3X, reducing delays and improving turnaround time for critical approvals.
OCR and large language models structure unstructured prior authorization documents, and a rule engine validates the data, sustaining 95%+ extraction accuracy as caseloads expanded.
Get a 20-minute technical read from a senior engineer. No pitch deck, no sales team.
Microsoft 365 Copilot is a licensed assistant for Microsoft apps such as Outlook, Word, Teams, and SharePoint. A custom AI copilot is built around your own workflows, systems, and data. Microsoft's product handles email, documents, and meetings well. It cannot reach a proprietary claims platform or a customer-facing SaaS product without extension work. Many enterprises run both, and Copilot Studio sits in the middle for teams that want to extend Microsoft's assistant before building from scratch.
An AI copilot assists a person and waits for approval, while an AI agent plans and completes multi-step tasks on its own. Copilots suit work where a human must own the decision, such as approving a payment or sending a legal reply. Agents suit high-volume tasks with clear success criteria and a low cost of error. Many teams launch a copilot first, track how often people accept its suggestions, and grant more autonomy step by step.
A copilot scoped to one workflow and a few data sources usually takes three to six months to reach production. Copilots that span several systems and teams can take nine months or longer. Data readiness drives the schedule more than model work. Document quality, access rights, and API access to source systems decide most of the timeline. Agreeing on an evaluation set in the first weeks shortens the end, because everyone knows what "ready to launch" means.
You reduce wrong answers by grounding each response in company data, requiring citations, and testing against an expert-approved evaluation set before release. When retrieval finds nothing relevant, the copilot should say so rather than guess. Scoring tools such as Ragas measure faithfulness and relevance on every change to prompts or data. In production, user feedback and tracing show which questions fail, so the knowledge base or prompt can be fixed at the source.
Your data stays private when the copilot runs on enterprise model services inside your own cloud tenant and retrieval respects existing permissions. Azure OpenAI and Amazon Bedrock do not use customer prompts or outputs to train their foundation models. Permission-aware retrieval means a user only gets answers drawn from documents they could already open. Sensitive fields can be masked before they reach the model, and every prompt and response can be logged for audit.
Tell us the shape of the problem. A senior engineer reads it and replies. You won't get a templated capability deck.
