Before you spend, let’s talk sense, strategy & scalability.

Let’s unpack your AI idea, together

Share Your Concept
  • 80+
    In-house
    Experts
  • 5+
    Team’s Average
    Years of Experience
  • 93%
    Employee
    Retention Rate
  • 100%
    Project Completion
    Ratio
Our process

We follow a structured, yet informal 4-step consultation format

The AI use case scan

We discuss your product, operations, customers, and bottlenecks. Then, we map out use cases where AI can create visible value in the next 3–6 months.

Feasibility & readiness audit

We analyze your data availability, system integrations, and business workflows to validate if your idea is practically executable and what’s missing.

Build vs buy vs integrate strategy

We evaluate what's worth building custom, what can be integrated via existing platforms (e.g., GPT APIs), and what shouldn’t be pursued at all.

Business-ready roadmap

We provide a prioritized rollout plan, with timelines, effort, cost bands, and KPIs, so you can move forward with confidence.

AI walk & talk

Smarter AI starts with conversation

  • Tech Stack Language

    Powerful coding foundations that drive scalable solutions.

    Python

    Python

    R

    R

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    JavaScript

    TypeScript

    TypeScript

  • Cloud Services

    Secure, flexible, and future-ready infrastructure in the cloud.

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    Power BI

    AWS

    AWS (Amazon Web Services)

  • Interactive Data Tools

    Smart insights and visualization that bring data to life.

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    Tableau

  • Framework Extensions

    Enhanced tools and add-ons to accelerate development.

    TensorFlow

    TensorFlow

    LangChain

    LangChain

    Haystack

    Haystack

    Kubernetes

    Kubernetes

  • LLMs & GenAI

    LLMs & GenAI

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    OpenAI

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    Google Gemini

    Anthropic

    Anthropic

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    Meta LLaMA

Tech talk

Developer tips & insights

AI walk and talk

Enhancing conversations with real-time AI assistance

AI consultation provides real-time insights and assistance during the discovery phase. Leveraging natural language processing (NLP) and AI-driven analytics, it helps users capture key points, summarize discussions, and offer actionable recommendations on the go. Ideal for meetings, interviews, and collaborative sessions, this solution ensures productive, informed, and efficient interactions without interrupting the flow of conversation.

Your first consultation with us is free.
To judge feasibility, check three things: clear business objective (metric to move), data reality (do you have the signals needed, or can you collect them?), and integration surface (where will the AI show up in your product or ops). If any of these are fuzzy, especially data availability, start with a smaller analytics or rules‑based phase instead of full AI.
For 3–6 month wins in eCommerce, prioritize: search/recommendation tuning, marketing and lifecycle automation, support triage/chat assistance, fraud/anomaly detection, and internal ops copilots. These use existing data (events, tickets, orders) and plug directly into revenue, cost, or CSAT metrics, avoiding heavy model R&D.
Plan integration as “AI behind an API”: expose the model through a service that your apps, CRM, or ERP call just like any other microservice. Use adapters (iPaaS, webhooks, or small integration services) to connect to existing systems, and start read‑only (suggestions, scores) before letting AI write back or automate actions.​
Estimate ROI by pairing a specific metric lift with realistic volume: e.g., a 2–3% checkout uplift or 10–20% support time reduction multiplied by current revenue or cost baseline. For cost and timelines, think in phases: 2–4 weeks for discovery/prototyping, 4–12 weeks for MVP, and ongoing optimization, with budget bands that mirror a typical feature squad plus infra/LLM usage.
Default to API integration (GPT/Claude/cloud LLMs) when you need fast time‑to‑value, standard capabilities (summarization, Q&A), and low MLOps burden. Fine‑tune or build custom models only when your domain is highly specialized, quality must exceed generic LLMs, or data privacy/regulation forces on‑prem or VPC‑hosted models.

AI that works, not just talks.

Move from ideas to clarity with expert code reviews, platform choices, and piloted rollouts, no jargon, just traction.