Agentic AI Development agents that decide and act
Not another chatbot. We design, build and govern production AI agents that read your data, decide what to do, and act through your systems — on OpenAI, Anthropic Claude, Llama or on-prem LLMs, by an ISO 27001 certified AI engineering firm in India.
How It Works
Scope
Design
Build
Evaluate
Govern
What 'agentic' actually means here
A chatbot answers. An agent acts. Agentic systems perceive your data, reason about a goal, choose tools, and take actions through your APIs — then check their own work. We build the whole loop, with a human in it wherever the stakes demand one.
Agents we build
We start with one high-pain workflow where an agent pays for itself, prove it in production, then expand. Every agent ships with guardrails, logging and an owner.
Industries we serve
BFSI
KYC and document agents, fraud triage, customer-service automation — DPDP/RBI-aware.
Manufacturing
Quality-inspection agents, maintenance triage, OT/IT event response on the line.
Retail & QSR
Replenishment agents, footfall-to-action, dealer-portal and support automation.
Logistics
ETA-exception agents, dispatch triage, document and customs workflow agents.
Healthcare
Intake and records agents, prior-auth document intelligence — HIPAA-aligned.
SaaS / Tech
Support deflection, onboarding agents, internal knowledge and ops copilots.
Technologies & frameworks
Delivery lifecycle
Scope
Find the one workflow where an agent pays for itself. Define the action, the guardrails, and the success metric before any code.
Design
Agent topology (single vs multi-agent), tools, memory, the human-in-the-loop points, and the fail-safe behaviour.
Build
Working agent in a contained scope — typically a 2-4 week PoC against your real data and APIs.
Evaluate
Offline evals + shadow runs before the agent is allowed to act. We measure accuracy, escalation rate and cost-per-task.
Govern
Audit trail, model-risk policy, DPDP/GDPR alignment and decision rights — handed back to your team to own.
The economics — ROI Benefits
Typical time-to-PoC for a contained agent against your real data.
Reduction in manual effort on workflows an agent takes over.
Always-on execution — agents don't sleep, queue or take leave.
Model and code ownership — your IP, your repo.
Selected case studies
AI ticket-triage agent — IT helpdesk
70% of L1 tickets auto-resolved · MTTR down 55%.
Order-to-cash automation — distributor network
PO-cycle 14 days → 3 days · 60% reduction in invoice-dispute volume.
KYC document AI — NBFC
50K docs/month auto-processed · 3 FTE redeployed to higher-value work.
Agentic AI, not slideware
Modern automation isn't just RPA scripts. Agentic systems read your data, decide what to do, and call APIs to act. We build them on OpenAI, Anthropic Claude, Llama and on-prem private LLMs — with the guardrails and evals that keep an autonomous system trustworthy in production.
- Use-case selection — quantify the workflow's cost/time before building, not after
- Build-vs-buy — agent framework vs platform vs custom, chosen for your stack
- Guardrails-first — human-in-the-loop, output validation, PII redaction, rate limits
- Evaluation harness — you see accuracy, escalation rate and cost-per-task, not vibes
- On-prem option — private LLMs where data residency or sensitivity demands it
On-prem private LLM development
Run Llama, Mistral or fine-tuned models behind your firewall. We handle GPU sizing, RAG pipelines, observability and the secure SDLC around them.
Talk to our AI engineering teamSecurity & compliance
ISO 27001:2022 certified — agent engagements run under the same ISMS.
Human-in-the-loop by design for any action with financial, legal or safety stakes.
Full audit trail — every agent decision and action is logged and reviewable.
PII redaction and least-privilege tool access; secrets never sent to the model.
Compliance-aware — DPDP, GDPR, SOC 2, RBI / IRDAI sectoral alignment.
Why teams choose Proeffico for agentic AI
Frequently asked questions
What is agentic AI, and how is it different from a chatbot or RPA?+
A chatbot answers questions; RPA follows a fixed script. An AI agent is given a goal, then decides the steps, picks the right tools, and acts through your systems — adapting when reality doesn't match the script. The difference that matters in production is action: an agent doesn't just tell a human what to do, it does it (within guardrails) and verifies the result.
Agentic AI vs generative AI — what's the difference?+
Generative AI produces content — text, code, images. Agentic AI uses those generative models as the reasoning engine inside a system that also has tools, memory and the ability to take actions. Put simply: generative AI writes the email; an agent decides whether to send it, sends it, logs it, and follows up. Most real business value comes from wrapping a generative model in an agentic loop.
Which agentic use cases deliver ROI fastest?+
The ones with high volume, clear rules at the edges, and a measurable outcome: IT/customer-service ticket triage, KYC and document processing, order-to-cash and invoice handling, and replenishment. We typically start there — an agent that takes over a repetitive, well-bounded workflow pays for itself quickly, and earns the trust to expand into higher-stakes work.
How do you keep an autonomous agent safe and governed?+
Guardrails are designed in, not bolted on: a human-in-the-loop checkpoint for any action with financial, legal or safety stakes; least-privilege access to tools; output validation and PII redaction; full audit logging of every decision and action; and an evaluation harness so you can see accuracy, escalation rate and cost-per-task before and after go-live. Everything is built to align with DPDP and GDPR.
How long until we have a working agent?+
For a contained use case, a proof-of-concept against your real data and APIs typically takes 2-4 weeks. We deliberately scope the first agent narrowly so you can judge accuracy and ROI on real work before committing to a wider rollout.
Which LLMs do you build on, and can it run on-prem?+
We're model-agnostic — OpenAI GPT, Anthropic Claude, Llama, Mistral, or a private on-prem LLM where data residency or sensitivity requires it. The choice is driven by your accuracy, latency, cost and compliance needs, not by a vendor relationship on our side.
Who owns the agents and the code?+
You do — 100% of the model configuration and code lives in your repository. We build it with you and hand it back with the playbook, so your team can run, extend and govern the agents without being locked to us.
Ready to Get Started?
Let's discuss how we can tailor this solution to your business needs.
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