asaasinGenerative AI development
Asaasin builds generative AI for founders and VPs of engineering who need working software, not a demo: LLM features, retrieval over your own documents and agents that call your tools, with evals and guardrails built in. An entire dev team starting at $10K/month. 6 months of work in 60 days.
Generative AI development here means building the system around the model, not wiring an API key to a button. A model on its own has no memory of your data, no way to check its own answer and nothing between a bad output and the person who receives it. We build those parts, plus the screen, API or workflow that a person or another system actually uses.
Most engagements combine some of the following.
A model's answer can be wrong in ways that pass every ordinary test, so quality has to be measured, not felt. Every build ships with an evaluation suite: a fixed set of representative inputs with known-good outcomes, scored automatically before every deploy. A change to a prompt, a model or the retrieval layer either measurably improves the score or it does not ship.
Guardrails are rules enforced in code, not a line in a prompt asking the model to be careful. Where a wrong answer would be expensive, the model drafts and a named person approves. In the clinical build described below, both the voice-to-chart and the radiograph pipelines write into a pending-review state, and nothing becomes part of the record until a provider has read, edited and signed it.
AI-assisted code goes through the same gate as anything else the pod writes: a pull request in your repository, reviewed by the engineer who owns it, typed contracts and tests in CI. A model writing the first draft does not exempt it from review.
We are not tied to one vendor. The model is chosen per task on cost, latency and how well it holds an instruction, from Claude, OpenAI GPT, Gemini, DeepSeek, Grok, Llama, Mistral and Qwen. It sits behind a single interface in your codebase, so a deprecation or a better model is a config change and a re-test, not a rebuild.
Where it runs is set by your compliance requirements, not our preference. We deploy into your own cloud account or VPC, and open-weight models can run inside your own network so regulated data never leaves your perimeter. We have shipped a fully air-gapped build, with local models and zero external API calls. We do not train models on your data, and a model tuned on your own data is built only on request and stays yours.
Some generative AI work does not need a build. If you only need drafting or summarizing of public text, a feature in a tool you already pay for costs less than any engagement with us, and we will tell you so on the first call.
A custom build earns its cost in three situations: the value comes from data nobody else has, your workflow does not match what a generic tool assumes, or a compliance requirement such as a signed BAA, an audit log or data that must stay in your own cloud rules out a shared product. A pod is also the wrong shape for a two-week one-off, which a freelancer can do for less, or for a permanent hire who will own one codebase for years.
A Builder Pod runs one active build track: a pod lead plus a two-engineer bench, weekly ship, month-to-month with 30 days notice. A Growth Pod runs two concurrent tracks with a three-engineer bench, weekly ship and a bi-weekly strategy call. Enterprise engagements, three or more parallel tracks with a dedicated senior lead, are scoped in a meeting. One generative AI workflow with its eval layer is one track. A second workflow, or a review dashboard next to the first, usually makes it two.
There is no per-hour billing and no change orders. You are billed for capacity, and the pod lead who scopes the work is accountable for shipping it. A small build typically runs one to three months and a medium one three to twelve. A paused month is not billed, and we hold your pod seat so the same team picks the work back up.
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We dig into the data you have, the workflow you want to change and what a correct output looks like. No form gauntlet before the real conversation.
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A clickable version of the core flow, built against your real data where possible. You approve it or tell us what to change, and you keep it either way.
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A pod lead and senior engineers land in your repository and your cloud account, not ours.
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Working code ships every week, with the eval suite scored before every deploy. Standups run daily in your Slack, Teams or email.
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Repository, migrations, deploy pipeline and documentation stay in your accounts. Cancel with 30 days notice, and whatever shipped is already yours.
Client names withheld. Engineering described exactly as it shipped.
A provider's dictation becomes a structured SOAP note drafted into the chart, and a vision model reads radiographs. Both pipelines write into a pending-review state, so nothing enters the record until a named provider has signed it.
A language model grades each privacy policy against a fixed rubric and returns a typed status plus a list of specific fixes, so results reach the dashboard and outreach with no analyst reading them first. It grades and monitors; it is not a legal opinion or a certification.
An audit engine for payment data that cannot leave the building. Most of the detection runs in a deterministic rules layer, and a local model, run through Ollama inside the auditor's network, handles only the narrower cases where free text has to be read. Built and validated on modeled data, so the claim is what it detects, not a recovery figure.
An entire dev team starting at $10K/month. 6 months of work in 60 days. You pay for capacity, not hours. A Builder Pod runs one active build track with a pod lead plus a two-engineer bench, and a Growth Pod runs two concurrent tracks with a three-engineer bench. Enterprise, three or more tracks, is scoped in a meeting. Every pod is month-to-month with 30 days notice, and the prototype is free. Talk to us about pricing.
Most pods are working within five business days, and the first shipped work usually lands in week one or two. A small build typically runs one to three months and a medium one three to twelve. Past a year is rare, and it usually means the build has grown into a second track.
Every build has an eval suite: a fixed set of representative inputs with known-good outcomes, scored automatically before every deploy, so a change either measurably improves the score or is blocked. Guardrails are rules enforced in code, not a request in a prompt. Where a wrong answer would be expensive, the model drafts and a named person approves, and every tool call an agent makes is written to an audit log.
Claude, OpenAI GPT, Gemini, DeepSeek, Grok, Llama, Mistral and Qwen, chosen per task on cost, latency and how well the model holds an instruction. The model sits behind a single interface in your codebase, so when one is deprecated or a better one ships, it is a config change and a re-test rather than a rebuild.
No, we do not train models on your data. If you want a model tuned on your own data, we build that on request and it stays yours. You own all of it: code, data, models and documentation, with no license-back. If we stopped working with you tomorrow, nothing in the system depends on an Asaasin-only service to keep running.
Yes. We deploy into your own cloud account or VPC where your compliance requires it, and open-weight models such as Llama can run inside your own network. We have shipped a fully air-gapped system for a public-sector client, where every model call resolves to a local model and the pipeline makes zero external API calls.
It can be HIPAA-aligned under a signed Business Associate Agreement. There is no such thing as HIPAA certification, so we do not claim one. In the clinical AI build we shipped for a dentistry practice network, the model sits behind one internal interface, client data is not used for training, the system runs inside the practice's own environment, and a named provider signs every AI-drafted note before it enters the record. A SOC 2 Type II report is available under NDA on request.
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Thirty minutes to map your problem to a plan and a timeline. You will leave the call with scope, price, and a start date.