The following list includes their hourly charges, average ticket size, and their tech specialties to help you make a more informed choice when considering AI-driven solutions for your business needs.

1. Code B Solutions 

Platform Development for Mple AI by Code B

Code B partnered with Mple AI, a company focused on building secure, privacy-first agentic AI solutions for enterprises.

What they needed:

1. A full-stack product team to design, develop, and deploy their agentic AI platform from the ground up.
2. Secure architecture that could support modular AI agents with strict privacy boundaries.

How Code B helped:

• Took charge of the entire build, from architecture design and API layer to frontend UX and DevOps pipelines.
• Enabled real-time agentic decision flows and privacy-safe multi-agent orchestration, integrated with enterprise data ecosystems.

What worked for the client:

• MPLE AI moved from concept to a working agentic AI platform in under 6 months.
• The system now supports modular AI agents with enterprise-grade security and deployment flexibility
• Code B continues to serve as a technical extension of the founding team, evolving the platform alongside business needs.

We understand the need and scope to seek out a notable , quality software development team for a long-term partnership.

Visit a list of such software development experts in India—here.

In-House vs. Code B

AI Automation Capabilities & Time-Cost Comparison

2. SoluLab

Mobile Banking at Aman Bank by SoluLab

Our team looked into SoluLab’s recent engagement with Aman Bank , a major player in Libya’s financial sector.

The bank faced a few core hurdles:

1. Onboarding was tedious
2. Customer support couldn’t keep up with demand.

How SoluLab helped the bank:

• They overhauled how users interact with banking services altogether.
• Using AI, they introduced voice-command banking, automated KYC, and 24/7 digital assistants

What worked for the client:

• 50% reduction in the workload of human support agents.
• 60% decrease in average onboarding time.

If you're a fintech company looking for cost breakdowns, here are a few resources that can help:

1. Cost breakdown for trading apps development
2. Cost breakdown for banking app development

3. Markovate

Smart Retail Transformation at Aisle 24

Markovate partnered with Aisle 24 , a self-service retail chain known for its cashierless stores, to bring AI into play where it mattered most: the aisles.

The challenge was classic yet complex:

1. Real-time inventory blind spots, lack of customer-level insights
2. Operational processes were still stuck in manual mode.

Markovate tackled this with a mix of predictive inventory tracking, behavior-driven recommendations, and AI-led automation for daily tasks.

End result:

• A 3x sales lift
• Smoother shopping journeys
• Leaner operations.

For anyone exploring agentic AI development in retail environments, this project is a solid example of what execution with clarity looks like.

4. Clustox

Cost Tracking for Smarter Farming by Clustox

In a bid to tackle inefficiencies in modern agriculture, Clustox teamed up with WISRAN, a company aiming to make farming operations more transparent and cost-effective.

Company Agenda:

Help farmers move away from rough estimates and toward real-time cost visibility.

Solution blueprint by Clustox

Clustox built a tool that pulls data straight from the field

1. Equipment usage,
2. Fuel consumption
3. Labor metrics

All feeding into a live dashboard.

What worked:

Their ability to turn complex data into something farmers could actually use—a clean, intuitive interface backed by cost forecasting features.

The results speak clearly:

A 30% cut in operational costs and better day-to-day decisions on the ground.

5. ELEKS

Fraud Detection for a Financial Institution by ELEK

ELEKS worked with a financial institution aiming to strengthen its fraud prevention systems.

As fraud techniques became more advanced, legacy systems were falling short, flagging too many false positives while still letting some threats slip through.

ELEKS' solution for the problem

1. AI-driven models designed to catch suspicious activity as it happened.
2. What made the project stand out was how adaptive the solution was, learning from every transaction to refine its detection logic in real time.

The outcome?

A 95% improvement in fraud detection accuracy, fewer false alarms, and a noticeable uptick in customer confidence.

6. ThirdEye Data

Predictive Maintenance in Aerospace by ThirdEye

When we looked at ThirdEye Data’s collaboration with a public sector aerospace and defense client,

The focus was clear:

1. Minimize helicopter downtime
2. Improve flight readiness through smarter maintenance.

Solution offered by the agency:

• Instead of relying on rigid schedules and manual data checks, ThirdEye’s team deployed AI to read sensor data from critical components.
• Their models flagged micro-patterns that hinted at wear and tear before it became a real issue.
• On top of that, the system could estimate the remaining useful life (RUL) of each part, helping crews plan ahead.

What results did the plan bring to the table?

The shift from reactive to predictive maintenance not only reduced unplanned downtime but also improved safety margins

7. Code Ninja

AI-Powered Credit Card Recommendations by CodeNinja

CodeNinja has worked with Truffle, a fintech brand set on simplifying how users choose credit cards.

The core challenge

Most users are overwhelmed by options and unsure what fits their financial profile.

Solution offered by CodeNinja

• Using their in-house Hyper platform, CodeNinja built an AI-powered app that learns from user behavior and financial patterns to deliver card recommendations that actually make sense—no guesswork, no generic lists.

The outcome

• A significant boost in user interaction and app retention, thanks to the relevance of the suggestions.

8. Quytech

Smarter Diagnostics Through AI by Quetech

Quetech has worked with a prominent healthcare provider aiming to tackle a core issue:

How to manage growing volumes of patient data while supporting more accurate diagnoses.

Solution built

• Quetech built an AI-powered application that could comb through electronic health records, flag relevant patterns, and assist doctors in diagnosis and treatment planning.

What stood out was the system’s ability to integrate quietly into clinical workflows

The impact of the solution built

• Noticeable gains in diagnostic accuracy and a much smoother process for handling patient data.

We have listed a few global health-tech app development agencies basis on their location , hourly rates and development specialties in this -article

9. XillenTech

Building a Secure, User-Friendly Platform by Xillentech

Our team took a closer look at how Xillentech supported a telecom services provider looking to overhaul its digital platform.

Client requirement:

• Improve security, make it adaptable, and ensure the interface feels intuitive for users.

What stood out?

• Their ability to integrate these improvements without complicating the user journey.
• Xillentech responded by engineering a platform that reinforced backend security while delivering a clean, accessible frontend experience.

The outcome:

A tighter security posture paired with a boost in platform engagement.

10. Clover Dynamics

Smarter Analytics for Faster Decisions by Clover Dynamics

Clover Dynamics collaborated with AnalystPro, a company seeking a more intelligent way to handle data-heavy decisions.

The goal was to move beyond manual reporting and into a space where insights could be both automated and actionable.

Solution for the problem

Clover Dynamics delivered custom AI-powered software capable of automating complex analysis and surfacing real-time insights.

End result

• Better operational efficiency and sharper decision-making across departments.
• The system didn’t just collect data—it turned it into something useful, right when it was needed.

11. Vstorm

Accelerating Due Diligence with Agentic AI by Vstorm

We reviewed Vstorm’s collaboration with a U.S.-based telecom provider that was struggling with time-consuming due diligence procedures.

The aim was to cut down manual review cycles without sacrificing accuracy.

Solutions offered by Vstorm

• Vstorm introduced intelligent AI agents built to handle those repetitive, multi-step processes—automating what used to take weeks and delivering results in just minutes.
• The AI wasn’t just fast; it was sharp, identifying key data points with higher accuracy than traditional workflows.

The result?

A major reduction in turnaround time and operational costs.

12. Azumo

Custom AI Agent for Smarter Customer Support by Azumo

We examined how Azumo assisted a large enterprise (under NDA) looking to overhaul its customer support operations.

Their objective:

• Reduce the load on support teams while keeping the experience responsive and reliable.
• Azumo delivered a tailored AI agent that plugged directly into the company’s existing systems—no disruption, just faster support.
• It handled common queries, routed complex ones efficiently, and improved resolution time across the board.

The outcome?

Faster responses, smoother operations, and real savings on support costs.

More: A list of CRM application development companies all around the globe

How to Choose the Right Agentic AI Development Partner

Here are key quantifiable factors to consider:

Experience in Agentic AI Projects

• Look for teams that have launched multiple agentic AI products in the last 1–2 years.
• A verifiable project portfolio signals hands-on expertise with dynamic task execution, goal-driven agents, and decision-making systems.
• A partner with experience in building agent workflows, LLM orchestration, or multi-agent coordination is more likely to align with your needs from day one.

Project timelines interactions

• The average time taken to go from idea to deployment matters more than ever in AI development.
• Firms that regularly deliver MVPs or usable prototypes in under 8–12 weeks can help you test hypotheses and reach product-market fit faster.
• Ideal for startups and small teams with tight feedback loops and investor expectations.

Customization Capabilities

• Custom builds beat off-the-shelf models when the business logic or workflows are nuanced.
• Assess how often the vendor builds from scratch or adapts open models (like ReAct, AutoGPT, and LangGraph) rather than using rigid templates.
• This flexibility is critical when your goal isn't just automation but unique, decision-capable agents tailored to your ops.

Integration Proficiency

• You’re not starting from zero; you already use CRMs, ERPs, or internal tools.
• A reliable partner should show strong references of integrating agentic AI with enterprise systems and APIs.
• This ensures your agents don't operate in a silo but are embedded within your existing infrastructure, reducing transition friction.

Retention and Client-Centric Values

• High retention rates and repeat business are indicators of a partner who listens and delivers value.
• Ask if clients have continued working with them beyond the first version—because that signals long-term thinking, proactive improvement, and support post-deployment.
• Bonus if they can walk you through how past clients scaled their agentic systems over time.

FAQ's