asaasinAI development services
Asaasin builds custom AI systems, the data pipeline, the model layer, the agents, and the product surface around them, for teams whose value comes from proprietary data or a workflow no off-the-shelf tool matches. We start with a free working prototype, then a pod of engineers builds it in your own repository, starting at $7,500 a month.
A real AI development engagement is four layers, not one prompt wired to a button. The data layer ingests whatever you already have, a legacy database, a spreadsheet export, a stream of events, and gets it into a shape a model can use. The model layer sits behind a single interface in your codebase so it can be swapped, fine-tuned, or run offline without touching a dozen call sites when a provider changes terms or a better model ships.
On top of that sits an evaluation suite: a real test set that catches a regression before a customer does, instead of a demo that only ever gets run once. And then the product surface, the screen, the API, the Slack bot, or the agent a person or another system actually talks to. Skip any of the four and what you have is a wrapper around someone else's API, not a system.
We build all four together, because they fail together. A model with no eval suite drifts silently. A pipeline with no product surface never gets used. The engineers who write the ingestion code are the same ones who write the UI, so nothing gets lost in a handoff between teams.
If the task is generic, drafting text, summarizing a public document, a feature already inside a tool you pay for will do the job for less than any development engagement costs. We will say that on the first call if it is true, rather than scope a build you did not need.
A custom build earns its cost under three conditions: the value comes from data nobody else has, your workflow does not match what a generic SaaS tool assumes, or you have a compliance requirement, a signed BAA, an audit log, data that has to stay inside your own cloud, that a horizontal product cannot meet. Most of what we build sits in one of those three buckets.
We are also honest about scale in the other direction. A modernization program spanning a dozen departments and hundreds of engineers is a different problem than a scoped build, and calls for a different kind of firm than a pod. We tell you which situation you are in instead of trying to fit every problem into our own shape.
A model in production is a liability the moment nobody is watching what it does. Every build ships with an evaluation harness that grades outputs against a real test set, so quality is measured before release, not felt after a complaint. Where an agent takes action, it runs against your real systems through scoped tool use with guardrails and approval steps, and every call it makes writes to an audit log you can read.
Data stays inside your own cloud account or VPC where your compliance requires it, not inside ours. 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, the same way the rest of the system does.
Deployment inside your own environment, code review on every pull request, typed contracts, and tests in CI apply to AI-assisted code the same way they apply to anything else the pod writes. Nothing skips the gate because a model wrote the first draft.
Pricing is published, not quoted after a sales call. A Builder Pod is $7,500 a month: one active build track, a pod lead plus a two-engineer bench, weekly ship, month-to-month with 30 days notice. A Growth Pod is $10,000 a month: two concurrent tracks, a pod lead plus a three-engineer bench, weekly ship plus a bi-weekly strategy call. Enterprise engagements, three or more parallel tracks with a dedicated senior lead, are scoped in a meeting because the shape varies too much for a single number.
A small, single-track build typically runs one to three months. A medium build spanning multiple workflows and data sources typically runs three to twelve months. There is no hourly billing and no change orders, you are billed for capacity, and the pod lead who scopes the work is the same person accountable for shipping it.
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We dig into the data you have, the workflow you are trying to change, and what the system actually has to do. No form gauntlet before the real conversation.
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Clickable software built against your real data where possible, not a slide deck. You approve it or tell us what to change.
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Once the prototype holds up, we scope the build into tracks, a model layer, an eval plan, and a pod size sized to the work.
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A pod lead and senior engineers land in your codebase and your cloud account, not ours.
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Shipped code every week with daily standups in your channel. Cancel with 30 days notice, no long contract underneath it.
Client names withheld. Engineering described exactly as it shipped.
The firm sat on raw statewide voter files and federal contribution data with no way to turn tens of millions of rows into something a campaign could act on. We built a campaign operating system over a unified voter and donor graph, with ML turnout and persuasion scores per voter and new states onboarding through a single command.
Charting, imaging, and patient comms lived in three disconnected tools, and providers spent the visit documenting instead of treating. We built a practice-management platform with AI in the clinical loop: a dictated visit drafts a structured SOAP note into the chart, and a vision model reads radiographs.
A public agency needed to surface duplicate payments, contract-splitting, and shell vendors across millions of accounts-payable lines, but the financial data could not leave the building. We built a vendor-spend audit engine that runs fully offline, with eight fraud detectors over an ingest-enrich-detect-score pipeline.
The prototype is free. A Builder Pod is $7,500 a month for one active build track with a pod lead plus a two-engineer bench. A Growth Pod is $10,000 a month for two concurrent tracks with a pod lead plus a three-engineer bench. Enterprise, three or more tracks, is scoped in a meeting. All three are month-to-month with no per-hour billing and 30 days notice to cancel.
A small, tightly scoped build typically runs one to three months. A medium build spanning multiple workflows and data sources runs three to twelve months. A build running past a year usually means the scope grew too large for one continuous engagement, and the right fix is splitting it into phases, which we flag at scoping rather than mid-build.
Whichever fits the workload and your constraints, hosted or self-hosted. The model sits behind a single interface in your codebase, so moving to a newer model or a different provider is a config change and a re-test, not a rebuild.
Yes. We deploy into your own cloud account and VPC where your compliance requires it, and we have shipped a fully offline, air-gapped system for a public-sector client where the data could not leave the building.
No. If you want a model tuned on your own data, we build that on request and it stays yours, the same as the rest of the system.
You do, from day one. Everything ships into your own repository and your own cloud account, with no license-back to us. If we stopped working with you tomorrow, nothing depends on an Asaasin-only service to keep running.
Engineering capacity around what your team has already modeled: production data pipelines, a swappable model layer, an evaluation harness, deployment, and the product surface the model needs to actually reach users. A pod works alongside your team rather than replacing it.
It can be HIPAA-aligned under a signed BAA. There is no such thing as HIPAA certification, so we do not claim it. We sign BAAs on request and have shipped two HIPAA-aligned platforms in production, with a SOC 2 Type II report available under NDA.
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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.