Feasibility & strategy
Use-case validation, data readiness review, and success metrics — before GPU spend starts.
AI products built for outcomes your business can actually measure.
We ship RAG pipelines, agents, and fine-tuned models into production — not slide decks. Senior ML engineers integrate AI into your existing stack with evals, guardrails, and cost controls from day one.
- 120+ products shipped to production
- Weekly demos with working software
- Senior-only pods — no bait-and-switch
What we ship
AI that ships inside your product — not beside it.
Most AI pilots stall at demos. We build systems grounded in your data, integrated into your product, and measured against business metrics — with architecture your team can operate after we hand off.
Whether you're adding a copilot to an existing SaaS product, automating document-heavy workflows, or standing up an enterprise knowledge base, you get the same model: tight scopes, honest evals, and engineers who've shipped ML in production before.
Common engagements
- Enterprise RAG & knowledge bases
- AI agents & workflow automation
- LLM-powered product features
- Fine-tuned domain models
- MLOps & model monitoring
- AI feasibility & strategy audits
Capabilities
ML delivery, end to end.
One accountable pod owns feasibility through production operations — no handoffs between research and engineering.
Data & embeddings
Chunking strategies, vector stores, and ingestion pipelines tuned to your content and access patterns.
RAG & retrieval
Retrieval pipelines, reranking, and citation-aware answers your users can trust in production.
Agents & orchestration
Tool-using agents, workflow graphs, and human-in-the-loop patterns that fit real operations.
Fine-tuning & evaluation
Custom model training, benchmark suites, and regression tests so quality doesn't drift after launch.
Production MLOps
Latency, cost, and quality monitoring — with runbooks for prompt updates, retraining, and incident response.
How we deliver
From feasibility to production in six phases.
A cadence built for AI work — measurable progress every week, no demo-only deliverables.
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Assess
Map use cases, data sources, risks, and eval criteria. Exit with a go/no-go recommendation and realistic roadmap.
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Design
Model selection, retrieval architecture, and guardrail design — including fallback behaviour before users see output.
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Prototype
Working POC with benchmark results, cost estimates, and a path to integration — demoed every week.
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Harden
Prompt injection tests, PII handling review, and load tests — production readiness isn't a surprise at the end.
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Integrate
Embed AI into your product with feature flags, staged rollouts, and monitoring from minute one.
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Operate
Track quality drift, tune costs, and refine models on real usage — same pod, same accountability.
What you get
Tangible outputs at every milestone.
- AI feasibility report & success metrics
- Evaluation harness & benchmark results
- RAG / agent architecture & ADRs
- Production inference pipelines
- Prompt libraries & version control
- Guardrail & safety rule configs
- Cost & latency dashboards
- Ops runbooks & team handover
FAQ
AI & machine learning, answered honestly.
Straight answers before you sign anything.
No. We build RAG systems, document automation, classification pipelines, recommendation engines, and agent workflows — whatever matches the business problem. Chat is one interface, not the whole product.
OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, and open-source models via Hugging Face — we recommend based on latency, cost, data residency, and your compliance requirements, not vendor partnerships.
We design for least-privilege access, PII redaction, VPC-isolated inference, and audit trails. For regulated industries we align with HIPAA, SOC 2, and GDPR patterns — including on-prem or private-cloud deployment when required.
We define eval suites before launch — accuracy, hallucination rate, latency, and cost per query — plus business KPIs like time saved or conversion lift. Subjective "it feels good" isn't a release criterion.
Yes. We integrate with warehouses, document stores, CRMs, and internal APIs — PostgreSQL, Snowflake, SharePoint, S3, and more. We meet your data where it lives rather than forcing a rip-and-replace.
Feasibility audits can begin within 1–2 weeks. Full build pods typically kick off in 2–3 weeks. We'll tell you honestly if we're at capacity rather than overcommitting.
Next step
Ready to scope your AI roadmap?
Tell us what you're trying to ship. We'll respond within one business day with a clear next step.
NDA-First Approach
48hr Kickoff
5-Star Rated