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

Remote Generative‑AI DevelopmentA Dedicated DeveloperHourly or Monthly

Q: How is generative-AI development different from your chatbot or agent services?

Generative-AI development (this page) is about adding LLM features inside the product your team already ships - a summarise button, a draft-this assistant, a smart-classify, a structured-extract, a semantic search, a generate-from-template. Not a standalone bot, not an agent that takes multi-step actions. An AI chatbot (see /services/chatbot-development) is a separate surface that answers from a knowledge base. An AI agent (see /services/ai-agent-development) is a multi-step LLM workflow that plans, calls tools, and writes back to your systems. They overlap, and one engagement can cover more than one; the scoping call sorts out which you actually need.

Q: How much does generative-AI development cost - hourly or monthly?

Two ways. 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 Claude or GPT integration, or an evals pass. Or monthly at the standard $2,000 per dedicated developer for 160-172 hours of full-time, exclusive work in your repo and your cloud, with a 7-day risk-free trial - the better value when AI features are a rolling roadmap. Either way: a senior lead reviews and tests every release. The monthly rate is the same flat rate as every other Empiric Infotech engagement; no premium for the AI framing. Model and platform usage (LLM tokens, embeddings, vector DB) is billed to your own accounts at cost.

Q: Which models do you use - Claude, GPT, Gemini, open-source?

Whichever wins on your evals at a cost that works. Claude (Anthropic) is our default for long-context, tool use, and disciplined instruction-following; GPT (OpenAI) for cases where the latest model lineup wins; Gemini (Google) where multimodal or long-context discounts make sense; Mistral / open-source (Llama, Qwen) for cost-sensitive, self-hosted, or EU-residency cases. We support model swaps as a first-class operation - the prompt and integration stay the same; only the model and its config change.

Q: Which SDK or library do you use - LangChain, LlamaIndex, Vercel AI SDK, or hand-rolled?

Whichever fits your stack and the feature. The official Anthropic and OpenAI SDKs cover most single-purpose integrations and stay close to the API surface. The Vercel AI SDK on the front-end where streaming UI matters. LangChain when the orchestration is genuinely worth the abstraction. LlamaIndex when retrieval is the centre of the feature. A hand-rolled integration when the framework would be overhead. We pick on the scoping call and tell you why, in writing - we do not push a framework for its own sake.

Q: Do I need RAG, or just a single LLM call?

Depends on the feature. A summarise button on the page the user is already on does not need a retriever - the page's text fits in the prompt. A doc Q&A across a 10,000-page corpus does. A draft-this assistant in the editor often does not. A support-deflection chatbot does (see /services/chatbot-development). We will tell you which is which and not bill a vector DB you do not need.

Q: How do you measure whether the AI feature is working?

Eval-driven from week one. Golden sets of real inputs (anonymised) with expected outputs, adversarial sets that try to break the prompt, an LLM-as-judge for accuracy and faithfulness, your team grading edge cases. We measure cost-per-correct-answer, p95 latency, fallback rate, and per-feature spend - so you can tell whether a model swap helped or hurt, and whether the feature pays for itself.

Q: How do you keep the cost line under control?

A few layers. Per-feature observability so you can see exactly which feature, which model, and which user segment is driving the bill. Prompt caching where the model and surface support it. Smaller models on the easy cases and the bigger model only on the hard cases (a router). Structured outputs that do not waste tokens on prose. A model swap to a cheaper one when the evals say it wins. Rate caps and per-user budgets where the feature is user-driven. Honesty about which features are profitable and which are not.

Q: Where does the AI feature run, and who owns it?

You own it - your repo, your cloud account, your prompts, your model keys, your data - from day one. It runs where you want it: AWS, Azure, GCP, Hetzner, DigitalOcean, Hostinger, or on-prem. We work inside your accounts, not ours, so there is nothing to claw back if the engagement ends. Model and platform usage (LLM tokens, embeddings, vector DB seats) goes against your accounts at cost, not marked up.

Q: How fast can you start, and what if it is not working out?

Within 48 hours of sign-off: a scoping call, a written scope and team proposal, then onboarding - your repo, your cloud, a working local environment - on day one. The first 7 days on the monthly plan are a risk-free trial with a full refund if it is not a fit and a no-cost developer swap inside that window. On the hourly plan, you have already seen what an hour buys. After that it is monthly billing with 7 days notice to stop, or hourly with stop-any-time - no auto-renewal, no minimum term.

Generative-AI features inside the product your team already ships - summarise, draft, classify, extract, semantic search, generate - integrated by a remote dedicated developer for $25/hr or $2,000/mo, with prompt engineering, evals, and cost guardrails from week one.

GDPR / SOC 2 Aware

Anthropic / OpenAI / Google APIs

Self-Hosted or Vendor-Hosted

Generative-AI Features From Empiric Infotech LLP

Last updated: 2026-05-14

Empiric Infotech LLP integrates generative-AI features inside the product your team already ships - not a standalone bot, not a separate agent, not another tab. 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 data, a generate-from-template feature, a multimodal feature on top of an image or a chart - 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: 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, an eval pass on what is already shipped), or lock a month at the standard $2,000 for 160-172 hours of full-time, exclusive work when AI features are a rolling roadmap and the team needs a developer who knows the stack and stays on it. Either way the developer works in your GitHub or GitLab org, your cloud (AWS, Azure, GCP, Hetzner, DigitalOcean, or Hostinger), and your model keys (Anthropic, OpenAI, Google, Mistral, OpenRouter, or your self-hosted Llama / Qwen), with the LLM library that fits your stack (the official Anthropic and OpenAI SDKs, Vercel AI SDK, LangChain, LlamaIndex, or hand-rolled - chosen with you). We design the feature surface and the prompt, wire the API call and the streaming UI, structure the output (JSON schema, tool calls, structured generation), 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; if it is the back-office workflow, see /services/ai-automation-services.

$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

Not a demo notebook that does one nice thing on a slide. A production AI feature inside your real product, behind a feature flag, with the evals and the cost guardrails that turn a demo into a real product line.

An LLM feature inside your existing product, not a separate app

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, a generate-from-template feature, a multimodal feature on an image or a chart - integrated where your users already are, in your codebase, behind a feature flag you can roll out and roll back. We will tell you when a single Claude or GPT call beats a RAG pipeline beats an agent beats a fine-tune for what you are actually trying to do.

The right model, the right SDK, the right integration

Claude (Anthropic SDK) is our default for long-context, tool use, and disciplined instruction-following; GPT (OpenAI SDK) for cases where the latest model lineup wins; Gemini (Google) where multimodal or long-context discounts make sense; Mistral / open-source (Llama, Qwen) for cost-sensitive, self-hosted, or EU-residency cases; the Vercel AI SDK on the front-end where streaming UI matters. We wire it into your existing app server, your existing auth, your existing observability, your existing rate limits and your existing error handling - not a separate microservice that nobody owns next quarter.

Structured outputs and streaming UI

Where the feature needs structured data (a JSON object, a list of entities, a labelled result, a function call), the LLM returns JSON Schema-conformant outputs or uses tool calling - not a regex on a free-form response. Where the feature needs a fast-feeling UI, the response streams to the user token by token, with cancel, retry, and graceful failure built in. Where the feature needs both - tool-call to the right action, then a streamed answer - we wire that too.

RAG where it earns its place

Some features need retrieval (semantic search, support deflection, doc Q&A) - we build the RAG pipeline (chunking, embeddings, vector DB, reranking, citations) tuned for your corpus. Many features do not - a summarise button on the page the user is already on does not need a retriever, just the page's text in the prompt. We will tell you which is which and not bill a vector DB you do not need.

Evals on your real inputs, from week one

Eval-driven testing of the AI feature on your real users' real inputs (anonymised) - regression on every prompt or model change, accuracy and faithfulness graded by a stronger judge model and by your team, golden sets and adversarial sets, a clear cost-per-correct-answer line. Without evals, you cannot tell that the model swap helped or hurt; with them, you can.

Cost and latency observability per feature

A dashboard on per-feature LLM cost (input + output tokens, per model, per day), latency percentiles, error rates, fallback rates, and the cost-per-correct-answer line from evals. So you can tell whether a feature is profitable, whether the next model is worth the swap, and which user segment is driving the bill - per feature, not lumped together.

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 - instead of a one-off integration that rots when GPT-5 ships or your team's prompt got changed without a regression.

How we scope a generative-AI engagement

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.

A scoping call

Thirty to forty-five minutes. 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 if any, and what would count as a real outcome - users using the feature, time saved per session, conversion lift, support volume deflected. No charge, no obligation.

A written scope and team proposal

We send back the feature definition, the model and SDK we would use and why, the prompt design, the structured-output schema (or tool-call definition), the eval plan and the golden set, a rough cost-per-call estimate, the feature flag and rollout plan, who we would put on it - one developer to start, or a small dedicated team when there are multiple features and multiple surfaces - and the price both ways. We will tell you honestly when a single LLM call beats RAG beats an agent beats a fine-tune for what you actually need.

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

The developer gets into your repo and cloud 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, reviewed and tested by the senior lead - inside the first week. If it is not a fit by day 7, full refund on the monthly plan, no debate. On the hourly plan, you have already seen what an hour buys.

Hourly or monthly, your choice

Hourly: billed by the hour at $25, time tracked to the minute, a weekly time report and a demo, stop any time - best for a defined scope or a burst of work like a v1 feature, a model swap, an evals pass, or a Claude / GPT integration on a single surface. Monthly: 160-172 hours at the standard $2,000, monthly billing, cancel with 7 days notice - the better value when AI features are a rolling roadmap. Switch between them month to month as the work grows or settles; add a developer at the same rate.

Two ways to engage a generative-AI developer

Two ways to engage a remote generative-AI developer. 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 Claude or GPT integration on a single surface, or an evals pass. Or monthly at the standard $2,000 for 160-172 hours of full-time, exclusive work - the better value when AI features are a rolling roadmap, with a 7-day risk-free trial. Either way: your repo, your cloud, the right SDK and library for your stack, and a senior lead reviews and tests every release. Why the hourly premium? Generative-AI integration is high-iteration expert work - prompt engineering, eval design, model selection, cost and latency tuning, structured-output reliability; the monthly rate is the same flat rate as any Empiric engagement once you commit. Model and platform usage (LLM tokens, embeddings, vector DB seats) 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, time tracked to the minute, a weekly report and demo
Best for a defined scope (v1 feature, model swap, single-surface integration, evals pass); no monthly commitment, stop any time
Your repo, cloud, and model keys from day one
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 a month
The best value when AI features are a rolling roadmap - feature after feature, model swaps, eval iteration
Your repo and cloud from day one; the same flat rate as any Empiric engagement
7-day risk-free trial, monthly billing, cancel with 7 days notice
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 generative-AI engagements start small - a block of hours at $25/hr for a v1 feature or a single-surface integration, or a first month at $2,000 - with a working AI feature shipping inside the first week. When the AI roadmap grows (a second feature, a third surface, a chatbot alongside the in-product features, an internal-tools agent alongside the customer features), you 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 (LLM tokens, embeddings, vector DB seats) are yours, billed to your accounts at cost.

What the first 90 days of a generative-AI engagement look like

Whether you are booking hours or on the monthly plan, the shape is the same. Here is a typical first three months on a generative-AI build.

Week 1
Onboarding and the first AI feature
Repo and cloud-account 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 set, behind a feature flag, with logging on cost and latency - shipped and reviewed. On the monthly plan, day 7 is the risk-free decision point.
Month 1
An AI feature shipped to users
The first feature rolled out (behind a flag, then a fraction of users, then GA), the prompt and structured output tuned to the inputs you actually see, evals expanded as edge cases surface, cost and latency dashboards on per-feature spend and p95 latency, a fallback path on model errors, and feedback collection wired so the team can grade outputs. By the end of month one, a real AI feature is in front of real users with real numbers.
Month 2
The second feature, the second surface
The edge cases month one surfaced - smoothed, the prompt and output schema tightened, the second AI feature scoped or shipped (a second surface in the product, a second use case for the same model, a related feature that reuses the eval and observability scaffolding), and a model-fallback path for outages. The AI feature stops being a one-off and starts being a product capability.
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 a newer Claude, or to GPT, or to Gemini, or to a cheaper open-source for the volume), a prompt-caching pass to bring the bill down, a fine-tuning pass when the general model still misses on your specific task, a quality pass on the eval numbers, and the next AI feature or surface scoped. From here the developer is ahead of the product roadmap on AI.

A dedicated generative-AI developer - hourly or monthly - vs a fixed-price AI integration agency, a no-code AI-feature platform, or a freelance contractor

	Empiric Infotech (generative-AI developer - hourly or monthly)	Fixed-price AI integration agency	No-code AI-feature platform (Vellum, Humanloop, etc.)	Freelance contractor (Upwork, Fiverr, Toptal)
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 into your product, evals, and observability are on you	One feature built, then they are gone; it rots when a model changes or your prompt drifts
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 and maintaining it	$30-$150/hr, quality varies; scope creep comes out of your budget
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	An hourly quote, often optimistic
Where the AI 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 prompt runtime	Whatever the contractor knows
Prompt management and evals	Prompts versioned as code, evals from week one, regression on every model or prompt change	Per the spec; new evals are change orders	Platform-provided prompt management; eval quality varies; vendor-lock risk on prompt migrations	Often skipped unless you ask
Structured outputs and streaming UI	JSON Schema-conformant outputs or tool calls, streaming UI with cancel/retry, multimodal where the use case calls for it	Per the spec; advanced cases are change orders	Whatever the platform supports	Whatever the contractor knows
Cost and latency observability	Per-feature LLM cost (input/output tokens, model, day), p95 latency, fallback rate, and a cost-per-correct-answer line from evals	Per the spec; new dashboards are change orders	Platform dashboards on platform-mediated calls only	Usually skipped unless you ask
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	On you to review and verify
Who owns it	You - your repo, your cloud account, your prompts, your model keys, your data, from day one	You, on final payment	You own the configuration; the runtime and the prompt store are the platform's	You per contract - check the IP-assignment clause
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	Re-hire the contractor, or it stays broken
Time to start	48 hours	2-6 weeks (proposal, SOW, kickoff)	Days (sign up); a week or two of integration before it does real work	Days to weeks (post a job, review, interview)

Figures are typical market ranges, not quotes. Model and platform usage costs (LLM tokens, embeddings, vector DB seats) 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, depending on the feature complexity and the surfaces involved.

Working hours and meeting availability

Our generative-AI developers work 09:30 AM to 07:30 PM IST, Monday to Friday. A project manager is reachable 07:30 AM to 10:30 PM IST. Live overlap by region:

Region	Developer live overlap	PM available for meetings	What this means
USA East (ET)	1 hr 9:00-10:00 AM ET	9:00 PM previous day - 12:30 PM ET	Morning standup, then most of a working day's prompt, eval, and integration work shipped async before your day starts.
UK and Ireland (GMT/BST)	5-6 hr 9:00 AM - 2:00 PM	Full UK working day	Live prompt reviews, eval walkthroughs, and watching a feature ship together across the morning.
Western Europe (CET/CEST)	6-7 hr 9:00 AM - 4:00 PM	Full CET working day	Effectively a same-time-zone working relationship - live design, pairing, and review through most of the day.
Sydney and Melbourne (AEST/AEDT)	3.5 hr 2:00 - 5:30 PM AEST	12:00 noon - 3:00 AM next day AEST	Afternoon standup and live review, then overnight async builds, evals, and deploys.

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

Real production LLM work shows up in the parts a quickstart skips: structured outputs your product actually consumes, an eval suite that catches the day a model swap silently drops accuracy, a per-feature cost line, a fallback path on a vendor outage. An in-house AI engineer who can carry that runs roughly $9,200 to $13,300 a month once you add benefits, payroll tax, and equipment, and a strong AI-fluent one is hard to hire and slow to start. 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 - with the same person on your AI features the next month and the month after, 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 inputs; a single Claude or GPT call dropped in with no eval, so the day after a model swap nobody notices accuracy fell; 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 then frozen while the model lineup shifts. A dedicated Empiric developer has shipped AI and LLM features into products since the current wave began - retrieval, agents, structured extraction, model integration - and is still there next month when the model improves or the prompt needs a tune.

We have built web and mobile products since 2014 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 cost-per-feature line nobody else builds, a fallback path on model outages, a feature flag and a rollout plan instead of a ship-it-all-at-once, and the honesty to say when a single LLM call beats RAG beats an agent beats a fine-tune for what you are actually trying to do.

Empiric dedicated generative-AI developer

$2,000/mo

the standard flat monthly rate - 160-172 hrs full-time, exclusive; or $25/hr for a defined scope; senior-lead review on every release; 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

In-house AI engineer (fully loaded)

$9.2K‑$13.3K/mo

$110K-$160K salary + benefits + payroll tax + equipment - and rarely a full-time job 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 kind of LLM-and-tooling integration a generative-AI build sits inside.

Read case study

EdTech, German client

Vedu - AI image generation and TTS in an EdTech app

An AI-powered German language learning product built for a German client by a two-person Empiric Infotech team - the Flutter app, the flashcard and quiz engine, text-to-speech, and AI image generation wired into the content backend. The structured-generation and integration discipline a generative-AI feature needs.

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, exactly the shape this page covers.

Read case study

Ready to ship your generative-AI feature?

Tell us what feature you want shipped inside your 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 - hours per feature or what a month covers. Your developer starts inside 48 hours, and a senior lead reviews and tests everything before it reaches you.

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