Capabilities

About

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Capabilities

About

Let's Talk

AI Systems

Every part is hard. The connections are harder.

Each part of a production AI system is complex. The bigger problem is how the parts affect each other, often in ways that you do not expect.

We work across all of the parts, so we design for these effects from the start, not after they fail in production. Hover over any part to see what it includes.

Core capability

Evaluations

We define what “good” means for your use case and test it continuously. Each change and each new model must pass before release.

Eval suites & golden sets

Scenario, edge-case and safety tests from your real work define what “good” means.

Agent & trajectory evals

Each step, tool call and decision gets a grade, not only the final answer.

Calibrated grading

LLM judges and rule checks grade at scale, and expert reviewers calibrate them.

Release & upgrade gates

Each new model, prompt or change must pass your tests before it can ship.

Online evals & A/B tests

New versions run next to current versions on real traffic before full rollout.

Failures become tests

Each production issue becomes a new test case, so the same error does not occur again.

Data Engineering

We build the pipelines, storage and retrieval for your enterprise data. Your models get context that is clean, correct and current.

Ingestion & ETL

Batch and streaming pipelines move data from databases, documents, APIs and event streams.

Document parsing

OCR and layout extraction convert PDFs, scans and forms into structured text and tables.

Data quality & labeling

Pipelines validate and normalize data, tag PII and add labels or synthetic data when necessary.

Storage architecture

Warehouse, lakehouse and medallion layers, with hot and cold tiers set by use and cost.

Retrieval (RAG) pipelines

Chunking, embeddings, hybrid search and reranking help the model find the correct passage.

Data contracts & lineage

Each source has an agreed schema, so you know what an upstream change will break.

Ontology Design

We make a shared model of your business: its entities, relationships and rules. All your data, models and agents use the same definitions.

Draft — confirm this list

Domain modeling

Entities, relationships and rules come from how your business works, not from your database tables.

Knowledge graphs

Your domain becomes a connected graph that people, applications and agents can query.

Semantic layer & taxonomies

Each term, for example “customer” or “order”, has one definition that all systems use.

Entity resolution

Records for the same customer, product or supplier become one trusted record across all sources.

Ontology-aware agents

Agents use your business objects and rules, so their answers and actions stay consistent.

Versioning & ownership

Each change has a review, a version and an owner, so the ontology stays current with your business.

Harness Development

We build the runtime around the model: orchestration, tools, context and guardrails. Your agents do real work in a safe and reliable way.

Agent orchestration

Single agents or teams of planners, workers and reviewers work together across long tasks.

Tool integration (MCP)

Secure, scoped connections give each agent access only to the APIs, systems and data it needs.

Context & memory

A clear design controls what the model sees at each step and what it keeps between sessions.

Durable execution

Long workflows save checkpoints, so they continue from the last step after a failure.

Guardrails

Each output gets schema and policy checks, and risky actions run in a sandbox first.

Human-in-the-loop

Before a high-risk action, the agent stops and asks a person for approval.

Foundation

AI Infrastructure

We build the compute, serving, networking and release layer for your AI. It runs in your cloud, on-premises or in both.

Draft — confirm this list

Cloud, on-prem & hybrid

Deployment in your cloud account, your data center or both, as your policies require.

Model serving & GPU capacity

Open-weight and fine-tuned models run on correctly sized compute that scales with demand.

Private networking

VPC isolation, private endpoints and air-gapped options keep services off the public internet.

Infrastructure as code

Code defines each environment, so development, staging and production stay identical.

CI/CD for models & agents

Automated pipelines build, test and deploy each component, with fast rollback.

Reliability & disaster recovery

Autoscaling, regional failover and tested recovery plans keep critical workflows available.

Model Portfolio

We select, tune and route models from any provider. Each request goes to the model with the best quality, cost and speed.

Model selection

Candidate models get scores on your tasks and data, not on public leaderboards.

Routing & fallbacks

Each request goes to the best model, and traffic moves to another provider if one fails.

Fine-tuning & distillation

Small specialist models do narrow, high-volume work at lower cost and latency.

Cost & latency tuning

Caching, batching and the correct model mix keep cost within budget without a drop in quality.

Version pinning & migration

Model versions stay pinned, and each provider update gets a full test before migration.

Multimodal models

Vision, speech and document models work together with text models in one system.

System-wide

Observability

We make every part of the system visible while it runs. You see each step, call, cost and failure in real time.

Draft — confirm this list

End-to-end tracing

Each step, tool call and token is recorded, from the user request to the final action.

Health monitoring & alerting

Latency, errors and drift are measured against targets, with an alert when a limit is exceeded.

Cost & usage analytics

Cost and usage by feature, team and model show you where the money goes.

Incident response

On-call engineers use runbooks and root-cause analysis to fix failures, and each fix becomes a test.

Security

We protect the system from external threats. We control prompts, tools, data and access at every point.

Draft — confirm this list

Identity & access control

SSO and least-privilege access apply to people, agents and tools, and agents run in sandboxes.

Injection & exfiltration defenses

Untrusted input stays isolated and outgoing data is screened, so prompts cannot take control or leak data.

Red-teaming & pen testing

Adversarial tests attack models, agents and integrations before launch and at regular intervals after.

Encryption & secrets

Data is encrypted in transit and at rest, and credentials stay out of prompts, logs and code.

Privacy

We keep personal data only where it is necessary. We delete it when it is no longer necessary.

Draft — confirm this list

PII detection & redaction

Sensitive data is found and masked before it goes into models, prompts, logs or traces.

Data residency

Data is processed and stored only in the regions that you select, including at model providers.

Zero data retention

Provider terms and settings make sure that vendors do not store your data or train on it.

Retention & data rights

Clear limits control how long data is kept, and deletion requests apply to all copies.

Governance

We record every model and agent, its owner and its permissions. Clear rules show who can approve a change.

Draft — confirm this list

AI inventory & ownership

Each model, agent, tool and dataset is on record, and each one has a named owner.

Model & agent policies

Clear rules specify which models, agents and data you can use, and the system enforces them.

Change control

New models, tools and high-risk changes need approval before release, and each approval is recorded.

Audit trails

A tamper-evident log records each action, decision and data access in the system.

Compliance

We map your controls to the frameworks that apply to your industry. We also make the documents and evidence that auditors need.

Draft — confirm this list

Framework mapping

Controls are mapped to ISO 42001, SOC 2, GDPR, HIPAA and the EU AI Act.

Risk & impact assessments

Each use case has a risk tier, with its risks and mitigations on record.

Model documentation

Model cards and technical files stay current, in the format that auditors expect.

Continuous evidence

Automated tests check controls while the system runs and make audit-ready reports.

Next step

Bring in a partner, not a vendor.

Tell us where your AI work is stuck. We will tell you clearly if we can help, and what it will take.

Let's Talk