AI Agent Development Company in Pune

Pune agent projects are sized by how many systems the agent is allowed to write to. A scoped AI-agent proof of concept at Pune rates writes to none. A production single-domain agent (warranty-claim triage for a Tata Motors or Bajaj dealer network, a code-review agent for a KPIT or Persistent Systems engineering team, security-alert prioritisation for a Quick Heal SOC) writes to one. Multi-agent platforms with end-to-end orchestration, human-in-the-loop review gates, and full audit trails write to several. Scope drivers include a single agent or small multi-agent flow, a tool-call integration into one or two existing systems (JIRA, ServiceNow, Salesforce, an internal vehicle-telematics platform), a basic evaluation harness, and a hosted demo. Fixed-fee proposals.

ISO 21434 is the international standard for road-vehicle cybersecurity engineering, and UNECE WP.29 R155 (vehicle cybersecurity management) and R156 (software-update management) are the regulations type-approval authorities in EU, UK, Japan, and Korea now enforce on every new vehicle homologation. Tata Motors, Mahindra, Bajaj Auto export and OEM teams, and Mercedes-Benz India all live inside these regimes. When an agent drafts artefacts that feed an R155 cyber-security management case file or an R156 software-update procedure, every output needs full traceability — which model version, which prompt, which tool calls, which grounding documents, and which human review gate. We ship the audit trail as part of scope, set evaluation thresholds for the agent's drafting accuracy against a held-out validated set of approved artefacts, and gate any agent-produced submission through a homologation engineer's review. Final R155/R156 sign-off is the OEM's homologation team's call.

Yes. AIS-140 is the Automotive Industry Standard from ARAI (Automotive Research Association of India) that specifies the public-transport and commercial-vehicle tracking devices Indian fleet operators are required to fit. The data feed includes GPS position, ignition status, panic button, and a defined message format that flows into government Vehicle Tracking Platform (VTP) systems and fleet-operator dashboards. We build agents that triage AIS-140 anomalies — unexpected route deviations, panic-button alerts, prolonged idling patterns — and surface the right ones to fleet operations. The DPDP Act 2023 applies because driver and vehicle telematics is linkable to a person, so we ship an explicit consent and retention architecture as part of the build. Final AIS-140 device certification is the OEM's responsibility; our work sits above the device layer.

Often. KPIT runs deep automotive-software delivery for global OEMs including Daimler, Renault, and Honda from its Pune R&D centres. Persistent Systems runs global SaaS delivery for North American and European clients from Hinjewadi. Quick Heal builds endpoint and SOC products for the Indian SMB-and-enterprise market. The agent engagements at these companies are usually internal engineering productivity — code-review agents that catch known defect patterns in the KPIT AUTOSAR codebase, test-case generator agents for Persistent's SaaS clients, security-alert triage agents for the Quick Heal SOC that cut analyst Tier-1 load. We deliver against the company's existing SOC 2 Type II or ISO 27001 controls, integrate with their JIRA-and-ServiceNow-and-GitHub stack, and never expose proprietary client code to cross-border LLM APIs without an explicit Privacy Impact Assessment from the relevant client's procurement office.

Hallucination is the single biggest risk on agents that draft regulator-bound or customer-bound artefacts, and we treat it as the primary build constraint. Three defences combine. First, every agent operating on regulated artefacts runs in retrieval-grounded mode — the source documents (warranty policy, AIS-140 standard text, ISO 21434 case-file templates, R155/R156 regulation text) are indexed and the agent must cite passages, not invent claims. Second, we run an evaluation harness on every production push that measures hallucination rate against a held-out gold set drafted by domain experts. Third, every agent output that touches a regulator submission or a customer-facing decision routes through a human review gate — the agent drafts, a homologation engineer or a service manager reviews, and the agent learns from the deltas. We will not ship an agent that operates fully autonomously on a regulator-bound artefact.

A typical Pune AI-agent project ships in fourteen to twenty-four weeks from kickoff. Week 1 to 4 is discovery, regulatory classification (ISO 21434, UNECE WP.29 R155/R156, AIS-140, DPDP Act 2023 as relevant), tool-call surface mapping, and evaluation gold-set construction. Week 5 to 12 is agent build with LangGraph, CrewAI, AutoGen, or Agno depending on orchestration shape, with weekly evaluation runs. Week 13 to 18 is integration with the client's JIRA-and-ServiceNow-and-CRM stack or with Tata Motors-or-Bajaj-or-Mercedes-internal-systems for automotive scope. Week 19 to 22 is shadow-mode deployment running alongside the existing human workflow with no production-impact authority. Week 23 onward is gradual rollout with human-in-the-loop review gates. For agents touching homologation or regulator-bound artefacts add four to eight weeks for the OEM's internal validation cycle.

When a project genuinely needs novel research — multi-agent coordination on tasks the public literature has not solved, automotive-specific reinforcement learning on driving-policy data, or fundamentally new agent-evaluation methodology — we scope collaborations with COEP, Symbiosis, MIT-WPU's Faculty of Engineering, FLAME's Centre for Computer Science, or IIT Pune (the IIT Bombay extension campus emerging on the Pune outskirts) rather than overselling in-house capability. For standard agent work — RAG-grounded copilots, ticket-triage agents, code-review agents on known patterns, structured-output extraction — no academic partner is needed and we ship in-house. We are upfront about which bucket your problem fits into and will not pad the scope with research labour you do not need.

Most useful AI agents touch personal data at some point — a warranty-claim triage agent reads dealer-captured customer text, a code-review agent inevitably sees commit-author identifiers, a security-alert agent processes user-attributable log lines. The DPDP Act 2023 applies the moment that data is processed in or directed at India, and the Significant Data Fiduciary obligations may apply to large-volume deployments. We ship a DPDP data-flow map as part of every Pune engagement, segregate personal-data fields at the agent's tool-call boundary wherever possible (the agent reasons over anonymised proxies and only de-anonymises inside the human-review surface), and configure retention against both DPDP data-minimisation and the client's regulatory record-keeping obligations under ISO 21434 or SOC 2. Final DPO sign-off sits with your privacy officer.