Claude, GPT, and Gemini API integration built into your product - by a dedicated engineering team.

Remote Generative‑AI Development for USA A Dedicated Developer, Hourly or Monthly

Generative-AI features inside the SaaS, B2B, or consumer product your US team already ships - summarise, draft, classify, extract, semantic search - integrated by a remote dedicated developer for $25/hr or $2,000/mo on US morning overlap, W-8BEN-E provided.

GDPR / SOC 2 Aware

Anthropic / OpenAI / Google APIs

Self-Hosted or Vendor-Hosted

Generative-AI Features From Empiric Infotech LLP

Empiric Infotech LLP integrates generative-AI features inside the product your US team already ships - the SaaS app, the B2B tool, the consumer product, the internal-tools console. A summarise button on a long document, a draft-this assistant in your editor, a smart-classify on inbound, a structured-extract from a PDF, a semantic search across your data, a generate-from-template feature - shipped as a feature in your codebase, behind a feature flag, with the prompt engineering, eval discipline, and cost/latency observability that turn an AI demo into a real product line. Two ways to engage a remote dedicated generative-AI developer, billed in USD with a W-8BEN-E on file: book hours at $25/hr for a defined scope (a v1 LLM feature, a model swap, a Claude or GPT integration on a single surface, a SOC 2-friendly evals pass), or lock a month at the standard $2,000 for 160-172 hours of full-time, exclusive work on US morning overlap when AI features are a rolling roadmap. The developer works in your GitHub or GitLab org, your AWS/Azure/GCP account, and your model keys (Anthropic, OpenAI, Google, OpenRouter, or self-hosted), with the LLM library that fits your stack (Anthropic and OpenAI SDKs, Vercel AI SDK, LangChain, LlamaIndex, or hand-rolled). We design the feature surface and the prompt, wire the API call and the streaming UI, structure the output (JSON schema, tool calls), add retrieval where the feature needs context, build the evals on your real users' inputs, ship behind a flag, monitor cost and latency per feature, and run the model swap when a newer or cheaper one wins. A senior team lead reviews and tests every release. Why the hourly premium? Generative-AI integration is high-iteration expert work; the monthly rate is the same flat $2,000 as any Empiric engagement once you commit. If your need is actions, see /services/ai-agent-development; if it is a chatbot, see /services/chatbot-development.

$25/hr

hourly, pay as you go

$2,000/mo

monthly, lock it in

Weekly

time report + demo

Senior-lead

review on every release

What a generative-AI engagement delivers for US teams

Not a demo notebook that does one nice thing on a slide. A production AI feature inside your real product, in your US cloud, behind a feature flag, with the evals and the cost guardrails a security and product reviewer can read.

An LLM feature inside your existing product

A summarise button on a long document, a draft-this assistant in your editor, a smart-classify on inbound, a structured-extract from a PDF or an email, a semantic search across your RDS or Postgres, a generate-from-template feature - integrated where your US users already are, in your codebase, behind a feature flag. We will tell you when a single Claude or GPT call beats RAG beats an agent for what you are doing.

The right model, the right SDK, the right integration

Claude (Anthropic SDK) is our default for long-context and tool use; GPT (OpenAI SDK) for cases where the latest model lineup wins; Gemini (Google) where multimodal or long-context discounts make sense; open-source (Llama, Mistral, Qwen) on Bedrock or self-hosted for cost-sensitive cases; the Vercel AI SDK on Next.js or React. We wire it into your existing app server, your existing auth, your existing CloudWatch or Datadog observability, your existing rate limits and error handling.

Structured outputs and streaming UI

Where the feature needs structured data (JSON Schema, a labelled result, a function call), the LLM returns conformant outputs or uses tool calling - not a regex on free-form text. Where the feature needs a fast-feeling UI, the response streams token by token, with cancel, retry, and graceful failure. Where the feature needs both, we wire both.

RAG where it earns its place

Some features need retrieval (semantic search, doc Q&A, deflection) - we build the RAG pipeline (chunking, embeddings via OpenAI / Voyage / Cohere, vector DB on Pinecone / Weaviate / Qdrant / pgvector, reranking, citations) tuned for your corpus. Many features do not. We will tell you which is which and not bill a vector DB you do not need.

SOC 2-friendly evals on your real inputs

Eval-driven from week one. Golden sets of your real users' inputs (anonymised), adversarial sets, LLM-as-judge accuracy grading, your team grading edge cases, regression on every prompt or model change. PII redaction at write time. An audit trail a security reviewer can read.

Cost and latency observability per feature

A dashboard on per-feature LLM cost (input + output tokens, model, day), p95 latency, fallback rate, and the cost-per-correct-answer line from evals. Logging to CloudWatch or Datadog, alerts to PagerDuty or Opsgenie. Per-feature, not lumped together, so you can tell which feature is profitable.

A developer who is still there next month

Models change, your prompts drift, your features grow. A dedicated engagement means the same developer ships the next feature, swaps the model, keeps the evals green, and tunes the cost line - month after month.

How we scope a generative-AI engagement for a US team

No multi-week sales cycle and no twenty-page statement of work. A call, a written scope, a trial, then hourly or monthly - your call. All on US morning overlap.

A scoping call

Thirty to forty-five minutes on US morning overlap. You tell us what AI feature you want shipped (the surface, the input, the output, the user, the success criterion), what product it goes inside, what model and SDK you are already using, and what would count as a measurable outcome - users using the feature, time saved, conversion lift, support deflected. No charge, no obligation.

A written scope and team proposal

We send back the feature definition, the model and SDK we would use, the prompt design, the structured-output schema, the eval plan and golden set, a rough cost-per-call estimate, the feature flag and rollout plan, who we would put on it, and the price both ways. We will tell you honestly when a single LLM call beats RAG beats an agent beats a fine-tune.

A 7-day risk-free trial (on the monthly plan)

The developer gets into your repo and AWS/Azure/GCP and ships the first slice - the feature working end to end inside your app on a small, real input set, the prompt and structured output in place, evals running on a golden set, behind a feature flag - inside the first week, reviewed and tested by the senior lead. Not a fit by day 7, full refund on the monthly plan.

Hourly or monthly, your choice

Hourly: billed by the hour at $25 in USD with a W-8BEN-E on file, time tracked to the minute, a weekly time report and a demo, stop any time - best for a defined scope or burst work. Monthly: 160-172 hours at the standard $2,000, monthly billing in USD, cancel with 7 days notice - the better value when AI features are a rolling roadmap. Switch between them month to month; add a developer at the same rate.

Two ways to engage a generative-AI developer

Two ways to engage a remote generative-AI developer, billed in USD with a W-8BEN-E on file. By the hour at $25 - pay as you go, time tracked to the minute, a weekly report and demo, no monthly commitment - best for a defined scope like a v1 LLM feature, a model swap, a single-surface integration, or a SOC 2-friendly evals pass. Or monthly at the standard $2,000 USD for 160-172 hours of full-time, exclusive work on US morning overlap - the better value when AI features are a rolling roadmap, with a 7-day risk-free trial. Either way: your repo, your AWS/Azure/GCP, the right SDK, and a senior lead reviews and tests every release. Why the hourly premium? LLM-integration work is high-iteration expert work; the monthly rate is the same flat rate as any Empiric engagement once you commit. Model and platform usage is billed to your own accounts at cost.

Pay as you go

Hourly plan

$25/hr

the premium short-burst rate - LLM-integration work is high-iteration expert work

A dedicated generative-AI developer, exclusive to you while you have hours booked
Pay as you go - billed by the hour in USD, time tracked to the minute, a weekly report and demo
Best for a defined scope (v1 feature, model swap, single-surface integration, SOC 2-friendly evals); no monthly commitment, stop any time
Your repo, US cloud, and model keys from day one; W-8BEN-E provided
Every release reviewed and tested by a senior lead before it goes live

Book a scoping call

Best value

Monthly plan

$2,000/mo

the standard flat rate - much cheaper per hour when AI features are a rolling roadmap

A dedicated generative-AI developer, full-time and exclusive - 160-172 hours on US morning overlap
The best value when AI features are a rolling roadmap - feature after feature, model swaps, eval iteration
Your repo and US cloud from day one; the same flat rate as any Empiric engagement
7-day risk-free trial, monthly billing in USD, cancel with 7 days notice; W-8BEN-E provided
A senior lead reviews and tests every release before it goes live

Book a scoping call

Larger or longer

Dedicated team

Custom

for multi-feature roadmaps or AI features across product lines

A small dedicated team - developers plus a senior team lead who reviews and tests every release
Add a developer (or a designer for AI-feature UI) at the same rate, in 48 hours
Pair a generative-AI developer with a chatbot or agent developer to ship the related surfaces at once
Best for multi-feature roadmaps, a multi-surface rollout, or shipping AI features across product lines

Talk to us

Most US engagements start small - a block of hours at $25/hr for a v1 AI feature, or a first month at $2,000 - with a working feature shipped inside the first week. When the AI roadmap grows, add a developer (or a designer for AI-feature UI work) at the same flat rate, in 48 hours, no re-contracting, and a senior team lead reviews and tests every release. Quality assurance is part of that lead's job, not an extra line item. Model and platform usage costs are billed to your own accounts at cost.

What the first 90 days look like for a US team

Whether you are booking hours or on the monthly plan, the shape is the same, all on US morning overlap. Here is a typical first three months.

Week 1
Onboarding and the first AI feature
Repo and AWS/Azure/GCP access, a working local environment, the model and SDK chosen, the feature surface mapped, and a first slice live - the AI feature working end to end inside your app on a small, real input set, the prompt and structured output in place, evals running on a golden US-flavoured set, behind a feature flag, CloudWatch logging on cost and latency - shipped and reviewed. Day 7 is the risk-free decision point on the monthly plan.
Month 1
An AI feature shipped to US users
The first feature rolled out (behind a flag, then a fraction of US users, then GA), the prompt and structured output tuned to the inputs you actually see, evals expanded, cost and latency dashboards (Datadog, CloudWatch, or Grafana) on per-feature spend and p95 latency, a fallback path on Anthropic/OpenAI outages, and feedback collection wired.
Month 2
The second feature, the second surface
Edge cases month one surfaced - smoothed, prompt and output schema tightened, the second AI feature scoped or shipped, a model-fallback path for outages, and the eval and observability scaffolding reused for the new feature.
Month 3 and on
Model swap, cost tuning, and ahead of the roadmap
A model swap if a newer or cheaper one wins on your evals (Claude to GPT, to Gemini, to a cheaper open-source on Bedrock), a prompt-caching pass to bring the bill down, a fine-tuning pass when the general model still misses, a quality pass on eval numbers, and the next AI feature or surface scoped.

A remote generative-AI developer - hourly or monthly - vs a fixed-price AI integration agency, a no-code AI-feature platform, or a US in-house hire

	Empiric Infotech (generative-AI developer - hourly or monthly)	Fixed-price AI integration agency	No-code AI-feature platform (Vellum, Humanloop, etc.)	Hire a US AI engineer in-house
What you actually get	AI features shipped inside your existing product, owned by you, with the developer who built them still there to grow and tune them	An AI feature built to a spec, then a maintenance retainer or you are on your own	A dashboard and a prompt UI; the actual integration, evals, and observability are on you	Whatever your team can build alongside their other work
Pricing model	$25/hr for hourly work, or the standard $2,000/mo for a full-time developer if you lock a month; model and platform usage billed to your accounts at cost	$10K-$60K fixed bid for a v1 AI feature; change orders billed extra	Platform subscription ($50-$1,000/mo) plus your team's time integrating	$140K-$220K salary + ~30% loaded - and rarely a full-time hire on its own
Estimate before you commit	An estimate both ways - hours per feature or what a month covers - plus a weekly time report and a demo	A fixed bid - you wear the overage as change orders	Platform demos; the real cost shows up after week two of integration	Internal estimates, if any
Where the feature lives	Inside your existing product, in your codebase, behind a feature flag - the developer works in your repo	Per the spec; sometimes a separate microservice you own, sometimes a closed black box	Behind a hosted API or SDK - the platform owns the runtime	Wherever your team builds it
Prompt management and evals	Prompts versioned as code, evals from week one, regression on every change	Per the spec; new evals are change orders	Platform-provided; eval quality varies; vendor lock-in on prompt migrations	As much as your team builds
Structured outputs and streaming UI	JSON Schema-conformant outputs or tool calls, streaming UI with cancel/retry, multimodal where the case calls	Per the spec; advanced cases are change orders	Whatever the platform supports	As much as your team builds
Cost and latency observability	Per-feature LLM cost, p95 latency, fallback rate, cost-per-correct-answer - SOC 2-friendly audit trail	Per the spec; new dashboards are change orders	Platform dashboards on platform calls only	As much as your team builds
Quality control	A senior lead reviews and tests every release before it goes live - built in, no extra charge	Per agency - often the same people who built it	On you to review and verify	Your own review process, if you have one
When the model changes (or breaks)	The same developer swaps the model, re-runs the evals, and ships the fix - book an hour, or it is in the monthly plan	A support ticket, or a new maintenance retainer	Wait for the platform to support it	Whoever built it, if they are still at the company
Time to start, and invoicing	48 hours; one monthly invoice in USD; W-8BEN-E provided	2-6 weeks (proposal, SOW, kickoff); per agency terms	Days; a week or two of integration before it does real work	2-5 months in a tight hiring market; W-2 payroll, benefits, payroll tax

Figures are typical US market ranges, not quotes. Model and platform usage costs apply on top of any build cost in every option and are billed to your own accounts in ours. A fixed-price agency build of a comparable LLM feature commonly lands in the $10K-$60K range before change orders.

Working hours and US overlap

Our team works 09:30 AM - 07:30 PM IST and a project manager is on call 07:30 AM - 10:30 PM IST, Monday to Friday. Here is exactly when that lands for clients in the US, Europe and Australia, your region first.

US Eastern (New York, Boston, Atlanta) - full team online 12:00 AM - 10:00 AM, project manager 10:00 PM - 1:00 PM (next day).Live cover across the edges of your day, your morning and your evening, with a project manager who replies the same business day.

US Pacific (Los Angeles, San Francisco, Seattle) - full team online 9:00 PM - 7:00 AM (next day), project manager 7:00 PM - 10:00 AM (next day).We are online through your evening and overnight, and your project manager sets live calls in your morning so you are never blocked.

UK & Ireland (London, Dublin) - full team online 5:00 AM - 3:00 PM, project manager 3:00 AM - 6:00 PM.

Central Europe (Berlin, Paris, Amsterdam, Madrid) - full team online 6:00 AM - 4:00 PM, project manager 4:00 AM - 7:00 PM.

Australia East (Sydney, Melbourne, Brisbane) - full team online 2:00 PM - 12:00 AM (next day), project manager 12:00 PM - 3:00 AM (next day).

Want it to the half-hour in your own time? Slide through your day and book a slot below.

Why US teams ship their generative-AI features with a dedicated developer, not a fixed-price agency

Hiring an LLM-fluent senior in a major US metro is the kind of cycle where the best candidates are already at Anthropic, OpenAI, or a Series-A AI startup, and a hire who can actually carry production AI features lands at roughly $14,600 to $19,800 a month once you add benefits, payroll tax, and equipment, after four to six months of open-to-start time. A fixed-price AI agency build of a v1 LLM feature typically runs $10,000 to $60,000 before the first change order, then a separate maintenance retainer. Empiric Infotech is billed two ways - $25 an hour for a defined scope, or the standard $2,000 a month per developer for 160-172 hours of full-time, exclusive work - in USD with a W-8BEN-E on file, with the same person on your AI features the next month, and a senior lead reviewing and testing every release at no extra cost.

Most generative-AI integrations fail in the same places: an impressive demo on a curated input set that falls over on real US-user inputs; a single Claude or GPT call dropped in with no eval; free-form text outputs that the rest of the product has to parse with a regex; cost lines that nobody is monitoring per feature; a v1 delivered the day the SOW closes and frozen while the model lineup shifts. A dedicated Empiric developer has shipped AI and LLM features in production for B2B SaaS, ops, and product teams - structured outputs, evals, retrieval, integration discipline - and is still there next month.

We have built web and mobile products since 2020 and AI/LLM features since the current wave began. The depth shows up in the parts a quickstart skips: structured outputs your product can actually consume, evals on real inputs from week one, a per-feature cost line nobody else builds, a fallback path on Anthropic/OpenAI outages, a feature flag and a rollout plan, a SOC 2-friendly audit trail, and the honesty to say when a single LLM call beats RAG beats an agent beats a fine-tune.

Empiric dedicated generative-AI developer

$2,000/mo

the standard flat monthly rate - 160-172 hrs full-time, exclusive, in USD with a W-8BEN-E; or $25/hr for a defined scope; senior-lead review; model usage at cost

Fixed-price AI integration build

$10K‑$60K

One-time fee. A v1 AI feature; change orders and maintenance extra; usage costs still yours

US in-house AI engineer (fully loaded)

$14.6K‑$19.8K/mo

$140K-$190K salary + benefits + payroll tax + equipment - and rarely a full-time hire on its own

Recent AI, product, and integration work

AI product, USA client

Superintelligence - an AI product engineered end to end

An AI product built for a USA client by an Empiric Infotech team - the application, the model integration, and the backend behind it. The shape of LLM integration this page covers.

Read case study

Travel and hospitality, USA client

Roamate - AI features inside a travel platform

A solo travel companion platform built for a USA founder by a two-person Empiric Infotech team - the Flutter app, the web surface, real-time chat, and the APIs and AI-assisted features behind them. AI features inside a real product.

Read case study

Consumer product, USA client

Velvet Passport - a US product engineered end to end

A US consumer product built for a USA founder by an Empiric Infotech team. The integration and platform discipline an AI-feature build needs - your repo, your cloud, your data, with the developer who built it still there next month.

Read case study

Ready to ship your generative-AI feature?

Tell us what AI feature you want shipped inside your US product - the surface, the input, the output, the user, what would count as a real outcome. Within 24 hours we will send back a feature definition, a model and SDK recommendation, the prompt and structured-output design, an eval plan, a feature-flag and rollout plan, a team proposal, and an estimate both ways. Your developer starts inside 48 hours on US morning overlap.

Who This Is For

Built for Businesses Ready to
Harness AI Creativity at Scale

We partner with founders, product teams, and innovators who want AI that doesn’t just automate - it creates. From generating personalized content to building adaptive AI tools, we make sure it works for your real-world needs.

This Is for You If:

You need AI-generated outputs that meet brand, compliance, or industry standards

You’ve tried ChatGPT or Midjourney but can’t scale quality or integrate results

You want AI that can create across multiple formats : text, image, video, or code

You’re looking for secure, private AI that learns from your data without leaking it

You want generative models fine-tuned for your audience, domain, or products

You’re done with one-size-fits-all tools and need a system tailored to your workflows

What We Do

We Build Generative AI
Systems That Create Like Experts, Operate Like Engineers

We don’t stop at “prompt engineering.” We architect full-stack generative AI solutions - from model selection and fine-tuning to API integration and deployment - all designed for accuracy, reliability, and scalability.

What We Build:

AI-powered content creation pipelines (text, image, audio, video)

Domain-specific fine-tuned LLMs for better accuracy & compliance

Intelligent content moderation, filtering, and fact-checking layers

Multi-format generation workflows integrated into your existing tools

Fully automated creative processes - from ideation to publishing

Platforms & Tools We Work With (and Beyond):

We’re platform-agnostic - if it can generate, we can integrate and optimize it.

Core Capabilities

Text-to-Anything Content Generation

Produce high-quality articles, ad copy, product descriptions, and more - tailored to your brand voice and optimized for SEO or engagement.

Powered by: Chat-GPT, Claude, Gemini, custom fine-tuned LLMs

Image & Creative Asset Generation

From photorealistic product images to AI-assisted illustrations and marketing creatives - generated in seconds, not days.

Built with: Midjourney, DALL·E, Stable Diffusion

Custom Fine-Tuned Models

Train models on your proprietary data to create industry-specific AI systems that understand your niche, tone, and workflow.

Tech behind the scenes: OpenAI fine-tuning, LoRA, embeddings, Pinecone

Multimodal AI Experiences

Combine text, image, and audio generation into unified tools - for example, AI that can write a script, create visuals, and generate voiceovers in one flow.

Built using: OpenAI, ChatGPT, Runway, ElevenLabs

Data-to-Insight AI Reports

Turn raw datasets into insightful summaries, visuals, and recommendations - without a single pivot table.

Integrated into: Notion, Data Studio, PowerBI, and custom dashboards

Our AI Solutions in Action

Real Creativity. Real Systems. Real Results.

Here’s what happens when we use generative AI to replace time-heavy, creative bottlenecks with systems that never get tired.

From Concept to Campaign in Hours

Generate ad concepts, copy, and creatives - all aligned to brand guidelines.

Used by: Marketing teams scaling campaigns without increasing headcount.

AI-Powered Knowledge Assistants

Turn manuals, SOPs, and archives into chat-ready knowledge bots that answer in context.

For: Support teams, training departments, and customer self-service portals.

Fully Automated Blog & SEO Engines

From keyword → article → image → publish - no manual drafts required.

Perfect for: Agencies, eCommerce, and content-heavy platforms.

Product Mockups at Scale

Generate multiple variations of product designs, packaging, and promo visuals instantly.

Built for: Startups and brands validating designs before production.

AI-Driven Research Summaries

Read, analyze, and summarize hundreds of documents or reports into a single actionable brief.

Designed for: Analysts, consultants, and decision-makers in fast-paced industries.

Want to see what generative AI could build for you?

How We Build Systems That Scale

A Collaborative Process, Built for Your Creative and Data Needs

We don’t just plug in AI APIs - we design, train, and optimize generative AI systems around your goals, workflows, and audience.

Discovery & Use Case Mapping

We explore your goals, datasets, and workflows - identifying where generative AI can replace manual work or unlock new capabilities.

Outcome: Clear ROI-backed AI roadmap

Data Preparation & Model Selection

We prepare your content/data, choose the right base models, and plan whether fine-tuning or prompt engineering is needed.

Outcome: Optimized, domain-specific AI foundation

Prototype & Validate

We create a functional prototype to showcase capabilities and gather feedback early.

Outcome: Stakeholder alignment and proof of value

Full Development & Integration

We build and integrate the generative AI solution into your tools, systems, and workflows.

Outcome: AI running seamlessly in production

Onboarding & Creative Enablement

We train your team to use, adapt, and improve the AI’s outputs - ensuring long-term productivity.

Outcome: Confident, in-house AI adoption