Build AI Agents for Production, Not Pilots

We architect, govern, and scale enterprise-grade AI agents your board approves, finance defends, and customers happily adopt.

Credentials
ISO 27001 Certified

Oracle Partner

SnowflakePartner

AWS Partner

Capability
16+
Years of Experience
250+
Projects Delivered
95%
Customer Retention
200+
AI-first Engineers
WHO THIS IS FOR

For C-Suite Decision Makers Leading Enterprise AI Execution

GrowExx is an AI agent development company delivering custom AI agent development for BFSI, manufacturing, healthcare, retail, and logistics. Our multi-agent systems read, reason, decide, and act inside your existing ERP, CRM, and core platforms — not alongside them — with audit-grade governance built in.
The result is the rarest outcome in enterprise AI agent development: applications a board signs off on, finance defends, and customers happily adopt. Every engagement is scoped backwards from a defended ROI model — cost-per-inference, automation rate, error reduction, and payback window — before a single line of orchestration code is written.
How we put AI into Enterprise

Thee Layers. Pick the Mix that Fits

01
AI & Data Engineering

Data platforms, MLOps, RAG pipelines; the foundation your agent runs on.

02
Custom AI Development

LLM apps, fine-tuning computer vision, predictive models built for your domain

03
GrowExx Products
Recogent, Hirin, Readerr plug straight in.
What we deliver

Our End-to-End AI Agent Development Services

Agent Strategy & Use-Case Mapping

We rank your AI agent backlog by ROI, data readiness, and compliance load, then map exactly where agents belong across your workflows. From design to go-live, with governance; everything is baked in from day one.

Custom AI Agent Development

Our custom AI agent development services engineer single-task and multi-task agents tuned to your domain — finance close, claims triage, demand forecasting, code review, support deflection. Built on RAG, function-calling, and your choice of frontier or open-source LLM.

Multi-Agent Orchestration ​

Specialist agents that collaborate, hand off, and escalate — supervised by a planner agent and a critic agent.

Enterprise Tool & System Integration​

Agents are only as useful as the systems they touch. We integrate with SAP, Oracle, Salesforce, NetSuite, ServiceNow, Snowflake, Databricks, and 200+ SaaS tools via MCP, native APIs, and custom connectors — with full role-based access control.

Evaluation, Observability & Guardrails

Every agent ships with an eval harness, prompt-injection defenses, output validators, cost dashboards, and drift alerts. You see exactly what each agent decided, why, and what it cost — in real time, in a single pane.

Managed AI Agents Operations

Post-launch, we own the operational SLA, including model retraining, prompt tuning, regression testing, vendor migration, and audit readiness, under a fixed-fee MSA. Your team focuses on outcomes, not on chasing hallucinations at midnight.

Industries we serve

AI Agents, configured for how your industry actually runs

Four focus industries. Each with a specific playbook for AI Agents.

The Challenge

A retail bank’s reconciliation team matches 40,000+ daily transactions across core banking, card network files, and Nostro statements. Breaks sit in Excel for 10–14 days. Every flagged item needs an audit-defensible trail before RBI/SEC filing.

How AI Agents Help

We build a Reconciliation Agent on Claude Agent SDK + LangGraph that ingests SWIFT MT940, card scheme files, and core banking extracts; matches at transaction level using a fuzzy-match + LLM-reasoning pipeline; auto-resolves 85%+ of breaks; and routes the rest to an analyst with rationale and source records attached.

Deliverables

A production agent deployed inside your VPC. Eval harness tuned to your reconciliation rules. Versioned audit log (prompt, decision, evidence) per RBI/SEC requirements. Integration with Finacle/T24/Flexcube and your GL via MCP. Analyst review console with one-click approve/escalate.

Quantified Result

10 days → 2

Faster month-end close at a Tier-1 private bank in India.

The Challenge

An auto-components plant runs 14 production lines on PLCs feeding a SAP MES. When a defect spike hits, engineers spend 6–9 hours pulling PLC tags, quality logs, and shift notes to find root cause. Lines run hot or stop.

How AI Agents Help

We build a Root-Cause Analysis Agent that subscribes to MES alarms via MQTT, pulls correlated PLC tag history from OSIsoft PI, reads shift handover notes and 8D reports in SharePoint, and proposes a ranked root-cause hypothesis with evidence. A Maintenance Agent then drafts the work order in SAP PM and assigns by skill and shift.

Deliverables

RCA agent + Maintenance agent on the same orchestrator. PI Historian + SAP MES + SAP PM connectors. Confidence-scored hypotheses with linked telemetry. Auto-drafted work orders requiring supervisor sign-off. Eval suite tested against your last 12 months of incidents. ISO/IATF 16949 audit log.

Quantified Result

9 hrs → 22 min

Mean time-to-root-cause at a global auto-components supplier

The Challenge

A specialty-care network submits 1,200 prior authorizations a week across Aetna, UHC, and BCBS. Each takes 5–10 days, requires payer-specific clinical criteria, and ties up two FTEs. Denials run 18%, mostly for missing evidence — not clinical merit.

How AI Agents Help

We build a Prior-Auth Agent that reads the patient’s Epic chart (problem list, labs, imaging, notes), matches against the payer’s published medical policy, assembles the packet with cited evidence, and submits via the payer portal or 278 transaction. PHI never leaves your VPC. Every output is signed off by a clinician before submission.

Deliverables

Prior-Auth Agent integrated with Epic via FHIR and your clearinghouse via 278/275. Payer-policy retrieval index, refreshed weekly. Clinician review UI with policy citations and chart evidence side-by-side. HIPAA-compliant audit log of every PHI access. Eval suite scored against your last 6 months of approvals/denials.

Quantified Result

18% → 6% denial rate
Prior-auth turnaround at a US specialty-care network

The Challenge

A national fashion retailer runs 240 stores + e-commerce on Shopify Plus, with inventory in Manhattan WMS and orders in NetSuite. Support handles 9,000 tickets/week. Stockouts on top-100 SKUs hit 31% during promos because demand signals lag, and replenishment is weekly.

How AI Agents Help

We build two agents on a shared orchestrator. A Demand & Replenishment Agent forecasts SKU/store daily, watches POS in near real-time, and triggers cross-store transfers and reorders against margin and capacity guardrails. A Customer-Service Agent resolves “where’s my order,” size exchanges, and returns end-to-end across Shopify, Manhattan, NetSuite, and the loyalty CDP — escalating only edge cases.

Deliverables

Two production agents, one orchestrator. Forecast model + reinforcement-learning reorder loop tuned to your SKU history. Shopify/Manhattan/NetSuite/CDP connectors via MCP. Margin and stock-position guardrails enforced in the agent runtime. Auto-resolution + supervisor review queue. A/B harness for prompt and policy changes.

Quantified Result

48 hrs → 4 min
ticket resolution for National fashion retailer

See how we did 10 days → 2 for a similar BFSI client.

Industry-specific reference architecture. 20 minutes. No slides.

How we deliver

Five steps. One discipline. Enterprise AI agents that reach production.

Click any step to see what happens inside it and the tooling we deploy with.

Step 01 · Discovery

Map where AI agents actually belong in your enterprise.

We rank your agent backlog by 12-month payback, data readiness, and compliance load — and write the business case your CFO’s office will sign before a model gets touched.

Output: A defended ROI model and a prioritized backlog of the next enterprise agents

Step 02 · Design

Architect the agent system before a line of enterprise code ships.

We design orchestration, tools, memory, guardrails, and escalation paths — then red-team the system against hallucination, prompt injection, tool misuse, and runaway cost.

Output: An architecture review signed off by Security, Legal, and IT, plus a written eval plan tied to your acceptance thresholds.

Step 03 · Build

Engineer enterprise agents, evals, and human-in-the-loop in parallel.

Every commit runs through automated evals. Observability and the reviewer UX are wired in from day one — not bolted on after the demo.

Output: A staging-grade enterprise agent passing your acceptance benchmarks, with full eval coverage and observability live.

Step 04 · Pilot

Ship a controlled enterprise pilot — and harden before you scale.

We run against a pre-set acceptance threshold, then harden security, observability, and cost-per-task economics before broad rollout across business units.

Output: A production agent live inside your enterprise environment, plus a quarterly ROI review cadence with the business owner.

Step 05 · Operate

Own the agent’s compounding value across the enterprise, not just its launch.

Under a fixed-fee MSA, we own retraining, prompt versioning, drift detection, model-vendor migrations, and audit readiness — so the agent improves quarter over quarter instead of decaying after go-live.

Output: A quarterly board-grade scorecard — usage, freshness, cost-per-query, and payback.

Why GrowExx

Why Choose GrowExx for AI Agent Development

01

ROI Defended Before Code Is Written

We refuse projects without a defended payback model. If we cannot prove 12-month ROI on paper, we will not put it on a roadmap. That discipline is why our agents survive budget reviews — and why our clients renew.

02

Vendor-Neutral by Design

We are not paid to push GPT, Claude, Llama, or Gemini. Every engagement starts with a model-routing decision based on accuracy, cost, latency, and data residency — not vendor incentive. You walk away with a stack you can swap, not a stack you are stuck with.

03

Built-in Governance, not Bolted On

Audit trails, prompt versioning, output validation, PII redaction, and human-in-the-loop escalation are part of the foundation — not features added when legal pushes back. Pass internal audit and external regulator scrutiny on day one.

04

95% Client Retention

95% clients extend the engagement. Not because they have to, because we ship outcomes, not invoices.

05

Fixed Outcome Pricing Available

Tired of T&M scope creep? We offer fixed-outcome contracts on most AI agent engagements. You pay for results, we absorb estimation risk.

06

AI-Driven Development

Our 8-agent autonomous dev workflow cuts delivery cycles by up to 40%. Same quality, shipped cleaner, on shorter timelines.

Explore how AI Agent Development Services can help

Selected work

Real-World AI Agents Case Studies & Success Stories 

Explore our latest case studies to see exactly how we deliver ROI for brands just like yours.

What our clients say

The CFOs and CIOs we've worked with.

"

GrowExx is the only partner who refused to start coding until we agreed on the ROI math. That discipline is exactly why our reconciliation agent shipped on time, on budget.

CFO
Global Finance Group
AI Agents
BFSI
"

We had three AI vendors. Two showed us demos. GrowExx showed us the eval harness, the cost-per-task dashboard, and the rollback plan. That's why they got the contract — and the renewal.

VP Operations
Apex Enterprise
Enterprise AI
Logistics
"

Their team treats every agent like a regulated piece of software, not a science project. That's the difference between a pilot you brag about and an agent your auditor signs off on.

Chief Risk Officer
Vanguard Manufacturing
AI Agents
Manufacturing

Talk to the team behind these outcomes.

From our insights

Stay up to date with our recent posts

Featured Product

Recogent — an AI agent built for the reconciliation problem this page describes.

A pre-built AI agent, deployed in weeks. Handles bank, GL, AR, AP, intercompany, and fixed-asset reconciliation with AI that surfaces only the exceptions you need to touch.

Recogent· Live metrics
99%
Less manual effort
99%
Match accuracy
<4 wk
to go live
Related services

Where to go next.

Embed a domain-tuned copilot inside the apps your teams already use.

 

 

Production-grade GenAI for content, code, and customer experience.

 

 

 

C-suite advisory: roadmap, ROI, governance, and operating model.

 

Pre-production audit of AI features for security, compliance, and performance.

 

 

 

Frequently asked

FAQs about AI Agent Development Services

AI agent development services engineer autonomous software systems that perceive context, reason over goals, call tools, and act inside enterprise systems — with governance and evaluation built in. Unlike chatbots, agents execute multi-step workflows and are accountable to a P&L outcome.
Most AI agent pilot engagements are delivered within $25K–$100K, allowing organizations to validate business impact with controlled investment and measurable outcomes. For enterprises requiring multi-agent orchestration, advanced automation, and industry-specific AI platforms, engagements typically range from $150K–$1M+. Every proposal includes a defined ROI framework and payback analysis before implementation begins.
Most single-agent deployments reach production in 2-4 months. Multi-agent systems with deeper integrations land in 16-24 months.
We are vendor-neutral. We choose between GPT, Claude, Llama, Gemini, Mistral, and Qwen based on accuracy, cost, latency, and data residency. Frameworks include LangGraph, CrewAI, AutoGen, Microsoft Agent Framework, and the OpenAI Agents SDK, with MCP for tool integration.
Agents deploy in your VPC, on-prem, or in a BAA/DPA-covered cloud region. We ship with PII redaction, prompt versioning, immutable audit logs, role-based access control, and output validation. Growexx is ISO 27001 and SOC 2 Type II certified, with delivery teams trained on GDPR, HIPAA, and sector-specific regimes.
Under our managed AI agent operations MSA, we cover 24×7 monitoring, retraining, prompt tuning, regression testing, vendor migration, and audit readiness — with named engineers and quarterly board-grade ROI scorecards. Standard production SLA is 99.9% availability with defined response and resolution windows.
Yes. We have production integrations with SAP, Oracle, Salesforce, NetSuite, ServiceNow, Snowflake, Databricks, Workday, and 200+ SaaS tools via native APIs, MCP servers, and custom connectors. We also bridge to legacy mainframes via RPA where APIs are not exposed.
Every engagement defines 3-5 economic KPIs upfront, including automation rate, cost-per-task, cycle-time reduction, accuracy lift, and revenue impact, and reports them quarterly. We instrument cost-per-inference, cost-per-task, and drift signals so finance has the same level of visibility it has on cloud spend.
Most of our clients start there. We offer a 2-day Agent Readiness Workshop and an executive enablement program for product, engineering, and operations leadership. Co-build models, where our engineers pair with yours, accelerate internal capability while we deliver the first production agent.
On qualifying engagements, yes. We tie a portion of fees to measurable business outcomes — accuracy thresholds, automation rate, cycle-time targets, or P&L lift. We do not bet on every project; we bet on the ones where we have full control over data, integrations, and the eval harness.
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Share your business goals and workflows. We’ll identify the AI agents that can create the greatest impact, along with estimated costs, timelines, and expected ROI.

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Product Discovery And Delivery- At Odds With Each Other?

Product Discovery And Delivery At Odds With Each Other

From having a product discovery workshop to a product’s delivery (or execution), there are quite a few challenges that fall along the way of the team that works on its development. If the certainty of the digital product’s release grows to be uncertain along the lines of its execution, there is a higher chance of errors, and ultimately, risks – risks that could lead to a poor display of the product in the eyes of the consumer.

Let’s say, for example, two different companies are working on a digital product.

In the first scenario (Team 1), the team knows what they’re doing. They set up a product discovery workshop (either themselves or in coordination with a company that is able to help them streamline their resources), letting them set the baseline for their approach towards the delivery and the market. The problems and solutions are identified, allowing there to be clarity in the initial stages, and in turn, the delivery is planned accordingly. The team will be ready for the next steps and have a roadmap of what needs to be done and when. The execution will be done smoothly, allowing the team to work and deliver efficiently.

On the other hand, there is a different company working on the delivery of a digital product. However, unlike Team 1, there is no clarity as to what the product is meant to achieve in the market. They decide to skim through the process and are left with nothing but mere insights that give them a hint as to what needs to be done. Confusion is left in the minds of the team members about the objective and the end goal is either discarded or left unachieved.

To put it simply, we learn how accurately or inaccurately the discovery phase helps in envisioning the ideal digital product.

Let’s dive into a little more detail:

1. Discovery in detail

For starters, what is product discovery?

Product discovery is the process of running through a cycle of research that allows the team working on the digital product to understand their customers furthermore. A certain period of time is dedicated to this research, allowing the team to explore the question “how can I build the right thing?”

One of the most common errors that software teams can make is to take the approach of product delivery – one that answers the question “how do I build the thing right?” To answer these questions, you need to gain a deeper understanding of your business model.

As you can see, the two questions reap different solutions. The reason why product discovery is so important is mainly because it allows you to study the consumer. Even if you create a digital product, one that works well and does what you expected it to do, the product is deemed close to useless if it doesn’t have an audience waiting to use it.

Understanding customer solutions with the help of product discovery is the first step towards a well-thought-out launch. If you learn fast, you deliver faster

The Purpose Of A Product Discovery Workshop

Read: The Purpose Of A Product Discovery Workshop

2. Robust and scalable implementations

Okay, so we’ve learned about customer solutions but what comes next? Building a robust and scalable implementation of the digital product in the market.

It’s the dream (if not the need) of every product manager to release a product that’s going to be 100% ready for the market, clean, pristine, reliable and overall, the ideal solution to the problem of the consumer. But how much of this is really achieved? 

Most of the time, software teams are made to be involved in the ‘solution’ phase of the product, i.e. when the creation begins. The question arises – how do you build a flawless digital product without involving the creators amidst the brainstorming sessions? And will you, as a team, ever be able to achieve the result you desire without allowing them to be a part of product discovery?

If you want to be able to provide a consistently reliable digital product, you need to understand all the possible risks in the discovery stage itself – which is where the software team comes in.

3. Enough features in MVP?

When we speak about the list of risks that come attached with a product’s delivery, many factors contribute to it. Like how economies of speed trumps scale in digital transformation these days, especially after the pandemic.

As discussed earlier, even though a digital product’s potential might be of a grand level, the delivery normally doesn’t match up. Without the involvement of the software team through product discovery, most of the features needed to limit the risks of its delivery tend to go missing. Ultimately, this leads to the creation of a minimal viable product (MVP).

Many a time, this tends to be discouraging for both the company and the teams involved in the launch of the digital product. This is mainly because even though you’re practically putting yourself “out there” and exploring the feedback of the customers as soon as possible with the MVP, you’re also risking putting your company and team in a compromising situation where you allow the audience to believe that this is the best you could come up with.

Even though we hate to believe it, the first impression can last longer than it should. If the consumer does not feel encouraged enough to come back to the MVP as it enhances, then you’ve already lost out on the necessary features required for its launch.

4. Getting feedback without pushing the users

Realistically, an MVP is supposed to have ‘just enough’ features to keep a consumer interested. If your team has been able to work that out, then your launch could be done successfully – you’ll be able to gather substantial feedback and you can accommodate all the necessary changes.

However, a digital product’s launch isn’t just about the product – it’s also about the consumer. Think about it – do you have customers that pursue your business already? Or do you still have an audience to build?

On the first hand, if you have customers pursuing your business then you need to be careful about the risks attached to your MVP. The product is supposed to help you drive more customers in and not take the ones that you already have, away.

In the second scenario, if you don’t have any customers attached to your business, then you might feel the urge to try extreme measures to gain feedback – online forms, invitations, etc. However, we suggest following the protocol of a customer development program. Since this is a team composed of consumers that want to interact with digital products and give feedback, you hold a much lower risk of losing the trust of your potential audience.

Preparing a user journey map is one of the first steps needed in gaining the right feedback. This can help you streamline which review is important, which isn’t, as well as what problems were mentioned over and over again. When you gain a clear understanding of the product requirement, it’s easier to build a strategic wireframe.

5. Rapid experimentation and Discovery – II

The cycle of discovery and delivery goes hand in hand. Once you finish the initial brainstorming session, you go forward with a test launch with your MVP. The MVP drives feedback from a set of customers that allow you to learn what your product lacks.

Now, once you have the feedback, it’s important to get back to the drawing board. What comes next? Sure, all of the feedback is important but how much of it is feasible? Which team needs to get to work? Was a part of the feedback predicted during Discovery I? If yes, you already know the next steps – if not, it’s time to begin.

During the second stage of discovery, you will realise how quickly things can move as long as the right teams are involved. While stage 1 allows you to get your hands in the game, the second stage allows you to learn more deeply about trial and error.

6. Small, incremental changes to a complex system

Trial and error bring us to our next step, the small incremental changes you need to follow through with.

Generally, this is the step that brings the entire team together. Since all of you have already witnessed the launch and feedback of the MVP once, the ‘want’ for a successful product delivery becomes even more integral in the eyes of the teams. You want to succeed, which is where everyone puts in their all.

Software teams, design teams and more work together on tiny but substantial changes that determine the growth of the digital product from a minimal viable product to a product ready for the consumer.

Product Discovery Workshop: Improving Patients Journey Via Data Science

Read: Product Discovery Workshop: Improving Patients Journey Via Data Science

7. Delivering solid software

While the process might look simple, it can be difficult to implement.

As mentioned at the very beginning of this blog, we tend to rush the delivery of a product from the very beginning of its discovery and while this can be motivational for some, it leads to the failure of both discovery and delivery in most cases.

Look back at the digital product launches that you have carried out as a team. Once you come together and look back at a launch by considering all the hiccups that occurred and what the true potential was, you will be able to learn from your mistakes and know what to do better the next time.

The process of product discovery and delivery is a learning process. Since the scope is massive, the pressure tends to be at its peak always, as well. However, that is the fun of learning and growing with a team that wants to deliver perfection.

At GrowExx, we believe in bringing together teams through the run of product discovery and delivery to be able to execute in a way unlike any other. Through teamwork and mutual understanding, every team can bring forward a problem and solution the others might not even consider.

Through teamwork, we deliver.

 

 

Vikas Agarwal is the Founder of GrowExx, a Digital Product Development Company specializing in Product Engineering, Data Engineering, Business Intelligence, Web and Mobile Applications. His expertise lies in Technology Innovation, Product Management, Building & nurturing strong and self-managed high-performing Agile teams.
Product Management

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