Deploy production-ready AI infrastructures that think, learn, and scale. Developer X provides the foundational models and integration tools for high-stakes enterprise environments.
Seamlessly integrate multi-modal AI capabilities into your existing CI/CD pipelines with zero-latency inference and enterprise-grade privacy.
Conversational agents with RAG (Retrieval-Augmented Generation) capabilities, supporting 100+ languages and deep context windows.
Host, fine-tune, and deploy custom LLMs and diffusion models on a secure, private cloud infrastructure.
Proprietary vision models for complex data extraction from unstructured financial and medical PDFs.
Real-time personalization API that processes billions of signals to serve hyper-relevant content.
Forecasting engines that predict market shifts, churn rates, and anomaly detection with 94% precision.
Every deployment starts from a process that already exists. Select a team to see the workloads we see reach production first, and how the output is checked before anyone acts on it.
Engineering
Customer Support
Finance & Operations
Legal & Compliance
Revenue & Marketing
Add retrieval, evaluation, and serving to an existing stack without building the surrounding infrastructure first.
Grounded search across repositories, runbooks, and incident history, with citations back to the source
REST and streaming endpoints plus typed SDKs that sit behind your current service layer
Evaluation suites that run in CI, so prompt, index, and model changes are reviewed like code
Common first workload: an internal assistant scoped to a single documentation corpus.
FINTECH
Processing 1M+ transactions per second using our Predictive Analytics engine to flag anomalies with zero false positives.
HEALTHCARE
Using Document AI to digitize historical records and Generative AI to summarize patient histories for oncology departments.
E-COMMERCE
Recommendation Engines that adapt UI components in real-time based on browsing velocity and intent analysis.
The gap between a convincing demo and a system people rely on is mostly evaluation, access control, and operations. This is the delivery path our engineers follow on every engagement.
01
Pick one workload, assemble a labelled evaluation set, and agree on the quality bar and acceptable error rate before a model is selected.
02
Connect sources, chunk and index them for retrieval, and mirror existing permissions so answers never cross an access boundary.
03
Benchmark hosted and open-weight models against your evaluation set. Fine-tune only where it measurably beats prompting and retrieval.
04
Automated scoring, adversarial prompts, and human review for factuality, tone, and regressions, versioned alongside the prompts they test.
05
Managed inference, single-tenant VPC, or your own Kubernetes cluster, served behind versioned APIs with staged rollouts and rollback.
06
Trace every request, track cost, latency, and quality drift, and feed flagged outputs back into the evaluation set on a fixed cadence.
Controls that regulated teams ask about in the first security review, available from day one rather than as a later migration.
Run inference in your own VPC or on-premise cluster. Customer data is not used to train shared models, and retention windows are configurable per workspace.
SSO and SCIM provisioning, role-based permissions inherited from source systems, and append-only logs of prompts, retrieved context, and responses.
Configurable PII detection and redaction before text reaches a provider, region pinning for residency requirements, and per-source retention rules.
Approval queues, confidence thresholds, and fallback paths so low-confidence or high-impact outputs reach a person before they take effect.
Versioned prompts and datasets, regression gates in CI, input and output filters, and documented behaviour for out-of-scope requests.
Route each workload to a different model, switch providers without rewriting application code, and export prompts, indexes, and evaluation sets on request.
Language models are probabilistic, so we design around that rather than around it being solved. Each workload ships with a documented failure mode, a review step for consequential actions, and an agreed error budget that is monitored after launch.
Connectors, runtimes, and identity providers we deploy against most often. Anything with a documented API can be added through the integration SDK.
6–10 weeks
Median time from kickoff to a first scoped workload in production
40+
Production AI workloads running across customer environments
3
Deployment modes: managed, single-tenant VPC, or fully self-hosted
99.9%
Monthly availability target for the managed inference tier
Most evaluations start with deployment topology, data handling, and how output is verified. If your review needs detail we have not covered, our solutions engineers will walk through the architecture with your security team.
No. Customer content is not used to train shared models. Where a provider offers zero-retention inference we enable it by default, and retention windows for prompts, retrieved context, and outputs are configurable per workspace.
Responses are grounded in retrieved sources and returned with citations, low-confidence results are routed to human review, and every prompt or model change runs against a versioned evaluation set before release. Accuracy is reported per use case from that evaluation set rather than as a single platform-wide number.
No. Models are configured per workload behind a routing layer, so a provider change is a configuration change rather than an application rewrite. Prompts, evaluation datasets, and index definitions are exportable.
REST and streaming endpoints, typed SDKs, webhooks for asynchronous jobs, and OpenTelemetry traces for request-level observability. Sandbox keys are issued before production so you can benchmark against your own data first.
You do. Your data, prompts, retrieval indexes, evaluation sets, and any weights derived from your data remain yours and can be exported at any point during or after the engagement.
Get a custom architecture review from our lead AI engineers. We'll help you navigate the complexity of model selection and infrastructure scaling.
1-on-1 Engineering Consultation
Free Sandbox Environment Access
Migration Roadmap Assessment
