AI Agent Development Company in Hyderabad

We quote fixed-fee per phase rather than publishing bands, and proposals include inference-token budget projections so FinOps does not get surprised in month three. A scoped agent proof-of-concept (one workflow, one knowledge base, basic eval suite) on Azure AI Foundry, Bedrock Agents, or Vertex AI Agent Builder is a six to ten week engagement. A production multi-step planning agent adds tool use, human-in-the-loop checkpoints, guardrails, a LangSmith or Langfuse eval harness, and CERT-In incident integration over four to nine months. A pharma regulatory-affairs or pharmacovigilance agent adds USFDA 21 CFR Part 11 audit trail, electronic signatures, and validated environment IQ/OQ/PQ packs, scoped against how many GxP workflows are in play. A BFSI captive agent under RBI Master Direction adds model risk and concentration risk documentation. We quote fixed-fee per phase.

Yes. Our delivery model for Microsoft IDC, Google Hyderabad, Amazon Hyderabad, Salesforce India, and the BFSI captive clients aligns agent builds to the parent company's responsible-AI standard (Microsoft Responsible AI Standard, Google AI Principles, Amazon Trustworthy AI, Salesforce Trusted AI Principles) before the internal review board sees the artefact. We produce the impact assessment, the harm-and-mitigation matrix, the red-team test logs, the eval-suite results across fairness, harm, accuracy, and grounding dimensions, and the human-oversight design documentation in the format each parent's RAI office expects. We have seen these reviews from the inside through past engagements and we will tell you up-front if a use case is unlikely to pass the parent RAI committee, rather than billing six months of build only for the launch to get vetoed.

21 CFR Part 11 requires audit trails, electronic signatures with biometric or two-factor authentication where applicable, validation of computerised systems, and operational checks on data integrity for any electronic record submitted to or maintained under FDA regulation. For Dr. Reddy's, Aurobindo, Bharat Biotech, Divi's, Lupin, Hetero, and Granules, we deliver agents inside validated environments with documented IQ (Installation Qualification), OQ (Operational Qualification), and PQ (Performance Qualification) packs, audit-trail capture at every agent decision point with tamper-evident logging, electronic signature workflow integration with the client's existing CSV-validated identity provider, and an ALCOA+ data integrity assessment. The agent itself runs on a versioned model with frozen weights for the validated configuration, change control through a documented periodic review, and a regression eval suite that gates every model or prompt change before validated deployment. EU Annex 11 alignment is delivered in parallel when EMA markets are in scope.

Yes. T-Hub (one of India's largest startup incubators, anchored at IIIT Hyderabad campus), the Telangana State Innovation Cell, and the TSChE-supported smart-governance use cases generate steady agent demand. For T-Hub-incubated startups we scope to the venture-grade unit economics rather than over-engineering: a working MVP agent with cost discipline, an eval suite, and a runbook, scoped to survive a Sequoia, Accel, Peak XV, or Blume due diligence on the engineering side. For Telangana state agents (citizen services, T-App Folio integration, RTGS revenue records modernisation) we scope against the GFR-equivalent state procurement, the Telangana data-residency expectations, Telugu-English bilingual UX requirements, and Aadhaar-linked identity flows through the appropriate AUA/KUA or DigiLocker route depending on the use case.

Inference cost is the single most common surprise on production agents. We design FinOps in from the first sprint: route to the smallest model that passes the eval suite (often GPT-4o-mini, Claude Haiku, or Gemini Flash beat the flagship on cost without quality regression for many tasks), cache aggressively at the prompt and response layer, batch-process where latency permits, run open-weight Llama 4 or Qwen on dedicated GPU for high-volume use cases where the unit economics tip below hosted-API pricing, and instrument every agent step with OpenTelemetry token-count traces piped into Grafana or Datadog. Monthly FinOps reviews use an INR-denominated cost waterfall tied to specific Jira tickets so attribution survives auditor scrutiny. For BFSI captives we also document the inference-cost projection in the RBI Master Direction Concentration Risk Assessment because over-dependence on a single foundation-model provider is now a regulator-relevant concern.

Evaluation is what separates a demo from a production agent. We build a golden-set regression suite from day one (typically 100 to 1,000 scenarios depending on use case), run it through LangSmith, Langfuse, or Braintrust on every agent change before merge, and gate deployment on a documented pass-rate threshold. For RAG-anchored answers we score on grounding, faithfulness, retrieval relevance, and answer relevance through Ragas or a custom rubric. For agentic workflows we score on plan validity, tool-selection accuracy, parameter correctness, and successful task completion. Hallucination control combines retrieval grounding, structured-output schema enforcement through Instructor or Pydantic, refusal patterns for out-of-scope queries, and a human-in-the-loop checkpoint for high-stakes decisions. Adversarial red-teaming uses both manual probes and automated tools like Garak and Pyrit. We report eval-suite scores monthly to your engineering leadership rather than only at launch.

Yes, with the relevant model-risk and RBI Master Direction context. Goldman Sachs Hyderabad, Wells Fargo, Bank of America, JPMorgan, Deutsche Bank, Barclays, and the broader BFSI captive base in HITEC City and Gachibowli operate under their parent's global Model Risk Management framework (typically SR 11-7 for US-regulated parents, or PRA SS1/23 for UK-regulated) plus India RBI Master Direction on IT Outsourcing and the DPDP Act 2023 personal data obligations. We deliver agents with model-risk documentation that aligns to SR 11-7 expectations on identification, development, implementation, use, and validation, with challenger-model and benchmark-comparison evidence, and with the audit trail every captive's internal model-risk committee will demand. India-side documentation covers Concentration Risk Assessment under the RBI Master Direction, CERT-In 6-hour incident hooks, and DPDP Act 2023 personal data classification.

Honestly: we do not compete on research depth with Microsoft IDC's AI research team or Google Hyderabad's Gemini-aligned engineering. We compete on three things. First, build-and-handover capacity for prototype-to-production work that your in-house team is too senior to prioritise. Second, fixed-fee delivery with a defined eval-suite quality bar, which is harder to commit to with internal engineering loaded against multiple priorities. Third, faster time-to-deployment when the use case is well-scoped because we have shipped this pattern before. For genuinely novel research or for systems that need to integrate deeply with Microsoft Azure-internal or Google internal infrastructure, your in-house team is the right answer and we will tell you so on the first call. We are good at the well-scoped, production-grade, eval-driven build that needs to ship in a quarter rather than a year.