AI Platform Engineering
Software whose core runs on models and agents, engineered to production standard.
AI-native platform engineering is the design and build of software whose core runs on models and agents: the tenant model, model gateway, agent runtime, knowledge services, APIs, metering and governance that let many users and organisations use AI safely at scale. Solonomous provides it as an AI SaaS development service, engineering platforms for the organisations that will own and run them.
Whatever the platform, the same engineering sits underneath: answers grounded in your data, agents that act within limits, people who approve what matters, and releases tested before anyone relies on them.
AI for our dealer portal and our online store
AI for B2B Commerce
Dealer and distributor portals
AI for E-commerce
Online stores and shopping sites
Agentic Commerce
Buyable by AI agents
AI for Marketplaces
Many sellers, many buyers
Answers drawn from each tenant's own documents and records, with permissions enforced at retrieval.
Agents carry the tenant's identity into every tool call, and stop for a person where the rules say so.
Raise the support agent's monthly model budget for this tenant, with the same per-call limits.
Budgets, quotas and policies per tenant, with an audit trail for every decision.
One controlled path to every model, so changing a model is a tested configuration change, not a rewrite.
An AI-native platform is more than an interface on a chat endpoint. These are the layers we design and build, from the request at the top to the systems of record underneath; each is described below.
Identity, permissions and isolation for every organisation the platform serves. Per-tenant configuration decides which models, tools and data sources each organisation may use. Isolation is tested, not assumed: test suites try to reach one tenant's data from another's context, through the application, the retrieval layer and the agents.
One controlled path to every model: routing, budgets, fallbacks and records. Because every call passes through one place, adding or replacing a model becomes a configuration change tested against the evaluation suite, not a rewrite. The platform is never captive to one provider, and each tenant's data-handling rules are enforced before a request leaves.
Where agents plan, act, wait for approval and leave a trace. Built once as a platform capability, it lets product teams ship workflow builders, agent workspaces and embedded assistants on the same foundation and under the same controls, instead of rebuilding orchestration feature by feature. The patterns are the ones described under Autonomous Systems, applied across every tenant.
Grounded context from each tenant's own sources, with permissions kept. Lineage records what was indexed, when and from where, which lets a tenant correct a source and see the correction reach its answers. Knowledge graphs are added where relationships, not passages, carry the meaning.
How customers, partners and other agents build on the platform. Documentation, versioning, deprecation policy and backwards compatibility are engineering deliverables. Developer platforms, workflow builders and integration surfaces use the same gateway and runtime the product uses internally, so there is one set of rules, not two.
Applied at every layer: usage, limits, policy and audit. Governance is enforced at the gateway and the runtime, so a tenant's policies apply to every model call and every agent action, and reporting shows what was done, by whom, with which model and at what cost. Evaluation and observability run across tenants, so a regression is caught on the platform before a customer reports it.
Commissioned, and our own
Solonomous builds AI-native platforms in two ways. Commissioned: we design and engineer a platform for an organisation, from architecture to production, under that organisation's ownership and on its terms. Our own: we apply the same engineering to Solonomous products, beginning with WebAlly AI, an AI-native website platform under active development.
The two share engineering patterns, never a customer's code or data. A commissioned platform belongs to the organisation that commissions it; our own products are a separate business, listed under Products.
Every platform starts with one tenant and one workflow in production, then grows on the same foundation.
The platform's users, tenants, data boundaries, models and integrations are mapped, and the architecture is agreed in writing before code is written.
One tenant and one core workflow are built end to end through the gateway, runtime and knowledge services, with evaluation and monitoring live from the first release.
Further tenants, workflows and integrations are added on the same foundation; metering, quotas and governance are tuned on real usage.
Your team takes the platform over with its runbooks and evaluation suites, or our engineers keep operating it under an agreed scope.
Tell us who the platform serves, what it must do and what it must connect to. We will come back with an architecture and a way to build it.