Bring models, assistants and automations into your actual workflow
We wire OpenAI, Anthropic, Google, open-source and internal models into the parts of your product that matter. Embeddings, retrieval, agents and triggers land inside a secure data path, governed, evaluated and reviewed by senior engineers.
Scope my integrationPROVIDER MATRIX
Providers we integrate under one contract
Model choice is rarely single-vendor. We evaluate and wire the right mix for each workflow, production, staging and evaluation, with key management, rate handling and failover built in.
Governance layer
Scope the governance layer ↗ Most AI integrations fail at the approval path, not the model. We add the prompt registry, eval gate and review checkpoint before anything autonomous reaches production.
GOVERNANCE
Governance is the difference between a demo and a system
Model output is one layer. The layer that lets your team trust it is the one we build around it.
Versioned prompts with evaluation gates.
Prompts are code. Each change runs against a frozen evaluation set before it is allowed into production paths.
- Prompt registry with diff history
- Golden sets per task
- Regression alerts on model drift
Secure data paths, no surprise egress.
Access controls, redaction, retrieval boundaries and audit trails keep sensitive data out of places it should not go.
- Scoped retrieval with row-level filters
- PII detection before egress
- Per-environment key vault & audit log
A senior reviews the decisions the model cannot own.
Architecture, release, tool scope and autonomy limits stay with humans. We keep the approval trail, not a theatre log.
- Approval steps for high-impact actions
- Tool-use allowlists per agent
- Incident & rollback playbook
MEASURABLE OUTCOMES
What clients see after the integration lands
Indicative deltas from recent engagements. We report real numbers per engagement, not headline claims.
~62%
Time saved on tier-1 support
Scoped retrieval plus reviewed prompt chain replaced the first response layer for a B2B SaaS cohort.
3.4×
Analyst throughput on contract review
Document intelligence wired into the existing review tool; humans kept sign-off, model did the extraction.
-41%
Monthly inference spend
Model router sends each request to the cheapest provider that passes the workflow eval set.
0
Sensitive data leaks in audit
Retrieval boundaries and redaction enforced at the gateway; every outbound request is auditable.
Different route?
Integration isn't always the answer
If the model itself is the moat, proprietary data, custom training, private inference, integration of a vendor API won't get you there. Our AI engineering discipline covers from-scratch model work, owned data pipelines and specialized branches like computer vision or ML engineering.
This page · Integration
Ship AI into your existing product.
Vendor models + your data + your workflow. Two-week pilot, reviewed governance, measurable outcomes. You're in the right place, jump to intake below.
Go to intake Custom AI / Data
Own the model. Own the data pipeline.
Custom training, bespoke datasets, in-house inference. Machine learning engineering, data engineering, computer vision, the full AI discipline under one accountable team.
See AI engineering disciplineINTAKE ] Tell us enough to scope a two-week pilot
What should the model actually do
// task Automate contract review · Reply to tier-1 support · Extract structured data · Generate product copy · Classify uploads · Custom
// data path Public content · Internal knowledge base · Customer data (PII) · Contracts / legal · Code · Operational logs
// volume ~100 / day · ~10k / day · > 100k / day · Batch / episodic
// hosting Managed SaaS providers · Private cloud · On-prem / air-gapped · Mixed
Integration scoping
The model is not the hard part.The governance layer is.
We scope the prompt registry, eval gate and human approval path before any autonomy touches production, in a two-week pilot with named accountability.