We build custom AI systems, LLM integrations, computer vision, and predictive analytics for New York businesses, in the city that now hosts more AI company headquarters than anywhere else in the country. The same team stays on the whole way through, strategy, model work, integration, and everything that follows launch.
Built by a Team That Ships AI Products, Not Just Demos
New York doesn't just have finance and media anymore, it has more AI companies headquartered here than any other US city, which means the bar for a serious build partner keeps climbing. Every New York AI build runs entirely in-house here, discovery through deployment and monitoring.
A Roadmap You Can Set a Watch By
We build production-grade AI systems for New York businesses. Every engagement runs against a real milestone plan, with data readiness checked honestly before that plan gets locked in.
Eastern Time Hours, Genuinely Covered
Our teams keep dedicated India-US overlap hours structured around Eastern Time, so New York clients stay connected through Slack, Jira, and GitHub for the length of the engagement.
Built for Explainability, Not Just Accuracy
We design AI systems that can be audited and explained, not just ones that score well on a benchmark, because a model nobody can explain is a liability for a New York business, not an asset.
Ratings That Hold Up Past New York
Clutch and Google scores are earned across AI, mobile, and commerce work, delivered by engineers who build in RAG pipelines, computer vision, and production LLM integrations daily.
Platform Ratings
What our clients say across leading platforms.
Google, 4.9 out of five stars
4.9★★★★★★★★★★
Clutch, 5.0 out of five stars
5.0★★★★★★★★★★
GoodFirms, 4.8 out of five stars
4.8★★★★★★★★★★
What clients love about working with us
Milestones That Land When Promised
Accountability on sprints keeps milestones on schedule, with visibility the entire way through, never just at the finish.
Nothing Gets Buried in an Inbox
Planning stays live through dedicated overlap hours, not left sitting in an inbox somewhere.
Trusted With Real Production Data
Data-handling rules get set before development begins, not patched together after something goes wrong.
A Long-Term AI Partner, Not a Vendor
We design for drift from day one, which is exactly why most clients keep working with us long after launch.
Why New York Businesses Choose Akoode
New York now counts more headquartered AI software companies than any other US city, ahead of San Francisco and Austin combined, spanning finance, media, and enterprise software rather than one narrow niche. Every build here gets held to that same breadth of ambition, not a single-use-case mindset.
Eastern Time overlap. Our India-US delivery model is structured to provide dedicated overlap during New York business hours for sprint planning, model reviews, and deployment.
Pricing in USD. All figures are scoped and billed in US dollars, so currency is never a question.
Every model and pipeline here is written by our own engineers, nothing contracted out.
A single senior engineer owns the project the whole way, discovery through deployment.
Privacy by design, not by afterthought. HIPAA, FERPA, and applicable state privacy laws shape the build from day one, with your legal team involved wherever formal sign-off is needed.
Working Hours Built Around Eastern Time
Structured India-US overlap windows keep planning, model reviews, and deployment calls landing inside New York's own working day.
Built With AI Technology Chosen to Last
Long-term reliability under genuine traffic beats a trending benchmark, and that's how these choices get made. That keeps a New York AI system maintainable years after launch, not just at release.
Sector Depth Beyond a Single Vertical
Deep delivery experience across New York's dominant industries: Finance & Banking, Media & Entertainment, Retail & E-Commerce.
Support That Doesn't End at Deployment
Model monitoring, drift detection, and retraining continue after launch, so a New York AI system stays accurate as real-world data shifts, not just on demo day.
The Six Things a New York AI Engagement Actually Covers
Whether the client is a two-person New York startup testing a first AI feature or an enterprise retooling a core workflow, every engagement covers the same ground: strategy, data readiness, model development, integration, and support that continues past launch day. Expect $10,000 as a floor for simple API work, with enterprise or custom fine-tuned projects running $150,000 and up.
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AI Strategy and Discovery for New York Businesses
Most AI projects that go wrong go wrong before a single model gets trained, in the gap between what a New York business wants and what its actual data can support. We begin with a candid check on whether the data supports the ambition, not a roadmap quietly functioning as a sales pitch.
Data readiness and quality assessment before any commitment is made
Use-case prioritization based on real business impact, not novelty
Build-versus-buy analysis for off-the-shelf APIs versus custom models
A scoped technical roadmap with realistic milestones
Custom AI and Machine Learning Development in New York
When an off-the-shelf API can't do what a New York business actually needs, we build custom models: classification, prediction, and recommendation systems trained on real data, not a generic public dataset.
Custom classification, prediction, and recommendation models
Model training, validation, and performance benchmarking
MLOps pipelines for retraining as new data arrives
Explainability built in from the architecture stage, not bolted on
Generative AI and LLM Integration for New York Teams
We integrate LLMs into real business workflows for New York teams, RAG pipelines grounded in a client's own documents, AI assistants that actually know the business, and content generation tools that don't hallucinate their way into a compliance problem.
RAG pipelines grounded in your own documents and data
In-app AI assistants using OpenAI, Anthropic, and open-source models
Prompt engineering and evaluation frameworks, not guesswork
Guardrails and output validation built in from day one
Computer Vision Development for New York Businesses
From defect detection on a production line to document processing in a back office, we build computer vision systems for New York businesses trained on real operational images, not stock photo datasets that fall apart in production.
Object detection, classification, and image segmentation
Document processing and optical character recognition
Quality inspection and defect detection for manufacturing
Real-time video analysis for production environments
AI-Powered Automation and Predictive Analytics in New York
We build predictive models and automation that actually change how a New York business operates, demand forecasting, anomaly detection, and workflow automation grounded in real historical data, not a dashboard nobody acts on.
Demand forecasting and anomaly detection models
Workflow automation triggered by predictive signals
Recommendation engines tuned to real customer behavior
Dashboards built for decisions, not just reporting
A model that isn't monitored drifts, quietly, until it's making bad decisions nobody notices until the damage is done. We provide ongoing monitoring, retraining, and performance tracking so a New York AI system stays accurate as real-world data changes.
Model performance monitoring and drift detection
Scheduled retraining as new data becomes available
Cost and latency optimization for production inference
Flexible staff augmentation when an in-house team needs extra AI capacity
How an AI Project Gets Built in New York, Step by Step
Six stages that keep every New York AI engagement transparent and accountable, from the first data conversation through deployment and beyond.
Stage 01
Discovery and Strategy
Every New York engagement starts with a genuine assessment of what data actually exists and what state it's actually in, not an assumption that the data is ready simply because someone said it was.
Timeline
1 to 3 weeks
Most engagements find this stage runs longer than expected, because an honest data assessment genuinely takes time to do properly.
You receive
Data readiness and quality report
Use-case feasibility assessment
Technical scope document with realistic milestones
Risk register covering data, compliance, and integration risks
What'sActually Running Underneath a New York AI Build
Every tool gets chosen for real-world durability, not a trending score. OpenAI and Anthropic handle most generative needs without help; PyTorch and TensorFlow step in only when a task genuinely requires something custom.
PyTorch
TensorFlow
scikit-learn
HuggingFace
Results We're Happy to Show You
Real AI projects with results to show, model performance, adoption, and business impact, not a highlight reel.
AI-Powered Advertisement Catalogue Generator
Key Outcomes
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Production-Ready Templates
9
Visual Style Tones
Challenge
The creative production bottleneck in ecommerce and B2B marketing is not a talent problem. It is a process problem. Every product needs multiple ad formats. Every format needs channel-appropriate copy. Every piece of copy needs to stay on-brand across a growing catalogue. When that work is done manually, it does not scale, it does not stay consistent, and it cannot be reviewed efficiently when the output looks different every time a different person touched it.
What We Built
The brief required a modular AI pipeline that could take a product image, understand what it was selling and to whom, generate structured marketing copy across multiple formats, and render downloadable catalogue assets that looked the same every time. Deterministic output was non-negotiable. The system needed to produce layouts a marketing team could review, approve, and send without visual surprises or format inconsistencies between runs.
High-ticket real estate buyers do not convert the way e-commerce shoppers do. They research for weeks, visit multiple platforms, talk to multiple agents, and still leave most sites without taking any action. The platforms dominating Indian real estate search are built for discovery at scale, not for decision support at depth. For a consultancy where the average transaction involves crores, a website that shows listings and a contact form is not a business asset. It is a missed opportunity.
What We Built
The brief was not to build a listings website. It was to build a digital advisory platform where AI handled early buyer guidance, WhatsApp handled lead conversion, and the listings engine handled discovery. Every feature was mapped to a specific moment in the buyer journey where the previous experience was creating friction or losing the conversation entirely.
Radiology departments in emergency and trauma settings are caught between two pressures that pull in opposite directions. Imaging volumes keep rising. The tolerance for missed diagnoses stays at zero. A hairline cervical fracture overlooked during a busy overnight shift, or a pneumonia finding buried halfway down a full worklist, carries consequences that extend well beyond clinical error. Existing AI tools have added a third problem on top of both: they produce outputs without explanation, and clinicians who cannot see why a model flagged something are right to be cautious about acting on it.
What We Built
The brief was specific: a dual-stream diagnostic system that could detect cervical spine fractures at individual vertebra level and classify chest pathologies from X-rays, process each study in under two seconds, and present findings with visual explainability that clinicians could act on without second-guessing the model. Accuracy targets were set at specialist-comparable benchmarks. The clinical interface needed to triage automatically, not just classify.
Akoode Technologies has done a fantastic job developing a custom web application for my global real estate firm. They used Next.js and Node.js, which made the site incredibly fast and responsive, even on mobile devices. What really stood out was their deep research and data integration for different countries and cities, which added huge value to our platform. The design is modern, sleek, and user-friendly. From start to finish, their team was professional, supportive, and highly skilled. Yes, the pricing is slightly on the higher side, but the quality, speed, and long-term results make it completely worth it.
Ankit Goyat
Akoode Technologies is a total class act. They took on a very difficult job and completed it perfectly in a timely manner. I have made them my technical go-to resource and feel very fortunate to have found them.
Carl Bourhenne
What Our Client Says
Akoode Technologies has done a fantastic job developing a custom web application for my global real estate firm. They used Next.js and Node.js, which made the site incredibly fast and responsive, even on mobile devices. What really stood out was their deep research and data integration for different countries and cities, which added huge value to our platform. The design is modern, sleek, and user-friendly. From start to finish, their team was professional, supportive, and highly skilled. Yes, the pricing is slightly on the higher side, but the quality, speed, and long-term results make it completely worth it.
“
Ankit Goyat
AI Development Across 15 Industries
Akoode has delivered AI systems across the industries that make up New York's economy, tuned to real regional data rather than a generic default.
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Real Estate
Valuation and lead-scoring AI for real estate platforms in New York, trained on local market data instead of a flattened national average.
Senior-led delivery and a no-subcontracting model that gives New York clients direct access to the people actually building their AI system.
Awards & Recognitions
Recognised by leading platforms, startup ecosystems, and global technology communities.
Every Model Stays In-House, Start to Finish
The whole engagement runs inside our own team, nothing farmed out. The people writing your model's code are the same people on your calls, nothing filtered through a go-between.
AI Built to Earn Enterprise Trust
AI is core product from the start, never an add-on, and gets judged on production value, not pitch-deck polish.
One Senior Engineer Owns the Whole Build
A senior engineer leads every New York project, directly involved in architecture decisions, model reviews, and deployment, writing code alongside the team rather than managing tickets from a distance.
Trust-Grade Compliance From the First Sprint
HIPAA, FERPA, applicable state privacy laws, and any relevant sector-specific rule all get built into a New York AI system from the first design sprint, rather than left as a last-minute scramble before launch.
Talk Directly with Our Founder
Discuss your software vision, AI roadmap, and delivery strategy with the team leading product engineering at Akoode.
Straight answers on process, pricing, timelines, compliance, and what working with Akoode actually looks like for New York AI projects.
In 2026, most US AI work falls into three buckets: $10,000 to $25,000 for API integrations, $25,000 to $150,000 for custom builds, $150,000 and up for enterprise or fine-tuned projects. We won't commit to a figure until discovery's actually looked at your data.
This isn't a preference question, it's a fit question. APIs handle most generative work capably. Custom is worth it only once your data or task genuinely outgrows what a general model can do.
Size isn't the filter, scope is. First-time AI features for a startup, full transformation programs for an enterprise, both get the same attention to actual scope.
Our teams operate dedicated India-US overlap hours structured around Eastern Time, so New York clients get real-time collaboration during planning, model reviews, and deployment through Slack, Jira, GitHub, and weekly sprint reporting.
Shape is the real difference. Dedicated means a whole self-contained unit working your project. Augmentation means one or two people slotting into a team that already exists.
Start with the contact form here. It reaches a senior team member, not a general inbox, with a reply inside one business day. Where data is complex, discovery usually comes first.
We address this while planning the architecture, not as a fix added on later. Wherever a system touches health data, student data, or other regulated personal data, the relevant framework, HIPAA, FERPA, or a state privacy law, gets mapped directly into the model design, including a clear answer for how a given decision could be explained if a client is ever asked to justify one.
At minimum, monitoring, drift detection, infrastructure upkeep. From there, scheduled retraining as data accumulates, and latency or cost tuning once the system's handling real volume.
Ask about your data before you ask about timeline, because they're the same question really. Four to six weeks for straightforward integrations. Eight to fourteen weeks for custom models trained on your own data.
Mostly integration, occasionally standalone. Whichever it is, the AI work fits into what you've already built rather than sitting apart from it.
This is the rule, not the exception. We flag it honestly at discovery, and cleanup gets its own scope rather than getting crammed into an already-tight build.
Happens regularly. We audit what's already built first, model, pipeline, codebase, then assess risk honestly, then produce a plan before touching anything else.
Yes, always, before specifics come up. Everything produced during a project, code and models included, transfers to you completely once it's done.
Not a comprehensive one, and this genuinely surprises a lot of clients. There's no single federal AI statute currently in force. Instead, federal policy runs through a series of executive orders: the Trump administration revoked the prior Biden-era AI safety order in January 2025 and has since pursued an innovation-first approach, including a December 2025 order aimed at establishing a unified national framework and challenging conflicting state AI laws. At the state level, California's SB 53 is the most significant AI-specific law currently in force, though it applies primarily to large frontier model developers based or operating there. We build against whichever regulations actually apply to your specific situation, not a single assumed federal standard.
This is worth keeping, genuinely. Live data moves away from training data over time, quietly. Monitoring, drift detection, and a retraining plan are part of the offering, retainer or as-needed.
Our strongest sector experience in the New York AI market covers Finance & Banking, Media & Entertainment, Retail & E-Commerce, with data-handling and explainability standards built to what those industries actually require.
Reading for New York AI Product Teams
Practical guidance on AI strategy, model deployment, and technical decisions for founders and product leaders building with AI.
Tell us what you're building and we'll come back with a scoped estimate and a real recommendation.
Reply Time
< 30 working minutes
NDA-Friendly
Signed before kickoff
IP Ownership
100% yours from day one
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