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Applied AI for ops, support, and product teams

Production AI that cuts manual work — shipped in weeks, not quarters.

We build retrieval, automation, and agent systems inside your repo. First useful automation lands in 2–3 weeks, full rollout in 8–12. Senior pod, no hand-offs.

See what we ship

What teams get

First useful automation

2-3 weeks

Typical production rollout

8-12 weeks

Delivery model

Senior pod in your repo

Book a 30-min AI working call

Where our AI thinking comes from

Logos below represent communities our AI lead actively contributes to in research and applied AI safety, not commercial clients.

Elements of AIinterdisciplinary-collegefuturicetutkePARTIAL SPACEApartSwissaisafetycampBlueDot ImpactAalto UniversityEuropean Network for AI SafetyOrion PharmaUniversity of HelsinkiAISC
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What we shipstar

Five problem shapes we solve well

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Workflow automation

Replace repetitive ops work with measured automations. We track hours saved, not tickets closed.

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LLMs and custom models

Custom models and retrieval pipelines tuned to your data, with evaluations you can defend.

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Internal copilots and assistants

Domain-aware assistants for your team, scoped to one job they can do reliably.

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Content systems and personalization

Production content pipelines with quality gates — tone, accuracy, and brand checks built in.

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AI strategy and architecture

A two-week diagnostic that ends in a one-page plan: where AI pays back, where it doesn't, and what to build first.

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BENEFITSstar

What you'll feel in the first 30 days

Faster response times

Faster response times

Support and product surfaces respond in seconds, not minutes, with grounded answers your team would have given.

Hours back to your team

Hours back to your team

Repetitive ops work moves off humans. We measure the hours saved per workflow and report it weekly.

Built around your data

Built around your data

Models, prompts, and evals trained on your stack — not a horizontal SaaS pretending to know your business.

Scales without re-hiring

Scales without re-hiring

Volume goes up; headcount doesn't have to. Your unit economics improve with usage.

Fewer escalations, fewer mistakes

Fewer escalations, fewer mistakes

Guardrails, evals, and observability built in from day one. Quality is monitored, not assumed.

Decisions backed by your own data

Decisions backed by your own data

Internal analytics, dashboards, and copilots that turn raw activity into the next action.

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Work shippedstar

What we've actually shipped

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AI Assessment

A fully autonomous evaluation engine that understands the context of diverse question types—descriptive, essay-based, and more—to accurately score responses. Beyond just grading, it provides personalized, actionable feedback to help learners improve and grow.

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Gaussian Process Modeling for Industrial Applications

Developed enhanced Gaussian Process implementation combining neural networks with Stan for industrial fouling detection. Created monitoring systems to track model behavior and resolved complex framework compatibility challenges between Stan and GPyTorch.

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Review System

An autonomous AI-powered assistant that monitors and responds to customer reviews and ratings across your online listings (e.g., Google Maps, Play Store). It crafts contextually relevant replies to engage customers, enhance brand credibility, and build a strong, responsive online presence—without manual effort.

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RNA Structure Prediction using Deep Learning

Reviewed cutting-edge algorithms combining neural networks with thermodynamic principles to predict how RNA molecules fold. The research examined various methods including convolutional neural networks, bidirectional LSTMs, and hybrid approaches that integrate traditional dynamic programming with deep learning to address the NP-complete challenge of RNA structure prediction.

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Climate Emissions Analysis Platform

Developed full-stack data visualization platform using Python, Plotly, and Streamlit. Implemented predictive models with uncertainty quantification for reliable emissions forecasting, enabling data-driven decision making for sustainability initiatives.

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Epileptic Seizure Detection System

Led development of machine learning pipeline combining CNN and K-Means models for medical-grade seizure detection. Managed technical team through the complete development cycle, resulting in successful delivery to Orion Pharma and inclusion in Futurice's technical showcase.

How we think about AI

Most AI projects fail because they automate the wrong thing first.

We start with the smallest workflow that, if AI does it well, frees up real hours every week. Then we measure those hours back. No demos, no theatre — just the line that moved.

  • We map workflows before models. The model is the last decision, not the first.
  • We refuse projects where the cost of a wrong AI answer is higher than the value of a right one.
  • Every AI surface ships with an evaluation harness. If we can't measure it, we don't ship it.
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Testimonialsstar

What people say about us

Aayush's work has been indispensable to the progress of our project. He expertly set up and maintained organizational tools while providing sophisticated coding solutions throughout our machine learning pipeline. His technical expertise combined with his collaborative approach made him an invaluable asset to our research team.

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Dr. Rasmus Herlo

Dr. Rasmus Herlo

Post-Doc at University of Copenhagen

Aayush demonstrated exceptional analytical abilities in his research on AI tutoring systems. His systematic comparison of human and AI teaching patterns revealed critical insights that advance our understanding of educational technology. His work shows a rare combination of technical mastery and thoughtful consideration of human-AI interaction—precisely the skillset needed to build AI systems that truly serve human needs.

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Dr. Nitin Sawhney

Dr. Nitin Sawhney

Professor at Aalto University

Working with a technology partner is often a headache—requirements keep changing, and communication gaps are common.Futurebits is the only vendor I've worked with where documentation is so strong that nothing gets lost in translation. Their first-principles thinking and deep discussions help clarify evolving needs.They tick all the right boxes: extremely talented (Decoding Me's website and dashboard are loved by all), prompt, dependable, and truly trustworthy with time and money.

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Khushbu Chopda

Khushbu Chopda

Founder, Decoding Me

Futurebits delivered exceptional work ahead of schedule, making the entire experience truly seamless. Their adaptability to evolving requirements and collaborative approach greatly contributed to the project's success. A dependable and efficient partner—we sincerely value their contribution.

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Vinod Bombale

Vinod Bombale

Portfolio Manager - Global Pricing Innovation

Working with Futurebits has been a seamless experience. Their team is always receptive to our requirements, and quickly addresses any challenges that arise. They are proactive, engaging and focused to coming to a simpler, and more practical solution in all our web design & build requirements. We appreciate their collaborative approach and their dedication to ensuring our website runs smoothly.

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Ajay Menon

Ajay Menon

Senior Lead TechnoServe / Program Director Greenr

A team of self-starters through and through, Futurebits not only delivered high-quality work but also uplifted the entire team with their positive energy and collaborative spirit. Their creativity stood out—whether in layout, color schemes, or user flow. Every design choice reflected a deep concern for the end user’s experience. Our discussions were richer and more productive thanks to their thoughtful contributions and genuine enthusiasm.I wholeheartedly recommend Futurebits to any team looking for a talented, user-centered design partner that combines aesthetic sensibility with strategic thinking.

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Gopesh Mittal

Gopesh Mittal

Co-Founder Alphaquark

Futurebits is a detail-oriented design company with a strong understanding of the fintech space and provides valuable inputs to projects as needed.

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Pratik Ghosh

Pratik Ghosh

Founder Alphaquark

I had a fantastic experience working with Futurebits. From the initial consultation to the final launch, their team was professional, creative, and incredibly responsive. They took the time to understand my vision and transformed it into a beautiful, user-friendly website that truly represented my art brand.What impressed me most was their attention to detail, timely delivery, and willingness to go the extra mile to ensure I was completely satisfied.Highly recommended!

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Anita Rajwade

Anita Rajwade

Artist

Ways to work with us

Three ways in. All senior, all scoped.

Pick the smallest one that proves the bet. We'll tell you on the first call which model actually fits.

Sprint

2 – 4 weeks

A clear, scoped problem. Land one shipped artefact fast.

  • One shipped surface — a flow, a prototype, an automation
  • A short post-mortem with what we'd do next and why
  • Daily async updates, one weekly working session

Pod

8 – 12 weeks

A 0-to-1 launch or a meaningful 1-to-10 jump. We embed alongside your team.

  • A senior pod (design, engineering, AI) running end-to-end
  • Weekly demo cycle, fortnightly steering committee
  • Hand-off docs your team can keep running with

Partner

Ongoing

Long-running product or platform work. Compounding output, not vendor billable hours.

  • Senior team allocated as a fractional product unit
  • Quarterly OKRs tied to your business metrics
  • Right of first refusal on new bets, shared roadmap ownership

Not sure which fits? Most teams start with a Sprint and graduate.

Book a 30-min AI working call

FAQ

The questions everyone asks (and our actual answers).

What kind of AI projects do you ship?

Production AI systems for support, operations, and product workflows — including retrieval pipelines, evaluations, guardrails, and observability. We prioritize measurable business outcomes over demos.

Can you work with our existing engineering team?

Yes — most of our pods do. We pair with your engineers, write code in your repo, and follow your review process. The goal is your team is stronger when we leave.

How quickly can we expect ROI from an AI engagement?

Most teams see first useful automation in 2-3 weeks. Full production rollouts typically land in 8-12 weeks depending on integrations and governance requirements.

What does an AI engagement usually cost?

Sprints typically start in the low five figures USD; pods scale with scope and complexity. We share indicative pricing on the first call and a fixed proposal within a week.

Do you replace our existing engineering team?

No. We pair with your engineers in your repo, follow your review process, and aim to leave your team stronger than we found it.

Still have a question? Ask Futurebits directly.

Aayush Kucheria

I lead AI at Futurebits. We build production systems — retrieval, agents, evals — for ops, support, and product teams. The work I'm proudest of: LLM behavior research, AI in healthcare, and applied modelling that actually shipped.

We take engagements where AI clearly pays back. If your problem is better solved another way, we'll tell you on the first call. No theatre.

- Aayush KucheriaAI Lead, Futurebits — production AI, evals, applied research.
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