Generative AI now writes, designs, and produces content well enough to be the core feature of a product, not just an add-on bolted onto something else. Akoode builds generative AI products and applications from the ground up: content generation tools, creative platforms, code and document generation, and custom LLM-powered products built around what generation is actually good at. If you already have a product and want generative AI added into it, that is a narrower scope, covered on our Generative AI Integration page.
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Client Retention
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A summarise button bolted onto an existing app is a feature. A product where generation is the whole point needs its own architecture, evaluation loop, and cost model from day one.
We build generation as a first-class product capability: the model, the prompting or fine-tuning approach, the review workflow, and the cost tracking, not just an API call wired into an existing screen.
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We build products and applications where generation is the core capability, not a feature request layered onto something that already exists.
Service 1 of 6: Custom Generative AI Product Development
Calling a model API is the easy part. Getting consistent, on-brand, reviewable output at the volume a real product needs is the part that takes engineering.
We treat generation quality as measurable, not subjective. Every project ships with an evaluation approach that tells you how often the output is actually good, not just how impressive the demo looked.
The novelty phase of generative AI is over. Here is what actually separates a generation product people keep using from one they tried once and abandoned.
What gets generated differs by industry: a listing description, a clinical summary, a compliance filing. What stays the same is the need for a human review step before anything goes out under your name.
We choose the model and fine-tuning approach based on your output type, quality bar, and volume, not whichever model has the best marketing this quarter.
LangChain
LlamaIndex
HuggingFaceMost generation products that underdeliver were built without ever defining what “good output” actually meant. We define that first, then build to it.
Choose how you want to work with us. Every model includes dedicated engineers, full IP ownership, transparent communication, and direct access to the people building your generation product.
Best for: scope that is already nailed down, and a price you want nailed down with it
Best for: products that will keep evolving long after version one ships
Best for: a skill gap today, or velocity you need by next sprint
Every generative AI product we build starts with a concrete definition of good output, not a demo meant to impress once.
Key Outcomes
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Verified Properties Listed
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Successful Closures
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What We Built
Key Outcomes
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Scalp Analysis Speed
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3D Simulation Output
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Generation that looks impressive in a demo and generation that is reliably good at scale are two different engineering problems. We build for the second one and measure it, rather than assuming the first one is enough.
Recognised by leading platforms, startup ecosystems, and global technology communities.
Practical thinking from the Akoode team on what makes generated content actually usable, and what it costs to get there.
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Straight answers on timelines, team shape, security, and how we plug into your existing delivery process.
Development means building a product or application where generation is the core capability, from scratch: a content tool, a creative platform, a document generator. Integration means adding a generative AI feature into a product you already have. If you are extending an existing app, our Generative AI Integration page covers that scope specifically.
Generative AI is the underlying capability: producing new text, images, or other content. A chatbot uses that capability to hold a conversation, and an agent uses it to take multi-step actions. A generation product is usually about producing an asset (a document, an image, a piece of copy) rather than a back-and-forth exchange.
It depends on the output type, the quality bar, and whether the approach needs fine-tuning or works with prompting alone. We quote a fixed price after reviewing your actual use case and a sample of what good output should look like.
A focused generation tool with one clear output type typically takes six to eight weeks from discovery to launch. Products needing fine-tuning or multiple output types usually run ten to fourteen weeks.
Yes, when volume and consistency needs justify it. For lower-volume use cases, a well-prompted general model is often cheaper and just as effective, and we will recommend whichever fits rather than defaulting to fine-tuning.
We design a human review step for anything that matters before it reaches a customer, and we track how often generation misses the bar so the prompting or model can be tuned based on real data, not guesswork.
Yes. Enterprise builds usually need tighter compliance review, cost controls at volume, and integration into existing content or publishing systems, all of which we scope during discovery.
We work with OpenAI, Anthropic, Stability AI, and open-source models depending on the output type, cost, and whether data needs to stay within a specific region or infrastructure.
Yes. The generated content, the prompts, and any fine-tuned model created for your project belong to you at the end of the engagement.
Yes. We set up quality monitoring at launch and offer ongoing tuning plans, since output quality can drift as your content needs evolve, and we recommend at least a quarterly review.
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