Services
AI engineering for enterprises that need it to actually work.
We help organizations identify where AI will deliver real value, build the solution, integrate it into existing systems, and hand it off so your team can run it. Every engagement is scoped to minimize risk and maximize the chance you see results quickly.
AI Discovery Sprint
2–4 weeksBest for: Organizations that know they need AI but are not sure where to start—or those that have tried and stalled.
In a focused sprint, our senior team works alongside your stakeholders to evaluate your operations, data, and pain points—and identify the AI use cases most likely to deliver measurable results.
What you get:
- Assessment of 3–5 potential AI opportunities mapped to your specific operations and data
- Feasibility and ROI analysis for each, ranked by impact and implementation complexity
- A concrete pilot proposal for the top-priority use case, including architecture sketch, timeline, and resource requirements
- An executive briefing your team can use to build internal support
Why it matters: Most AI projects fail because they start with the technology instead of the problem. Companies that lead with a specific business challenge—rather than chasing the latest model—consistently find better results. This sprint ensures you invest in the right opportunity from the start.
Typical outcome: A clear, defensible answer to “Where should we apply AI first?”—delivered in weeks, not months.
Workflow Pilot
6–10 weeksBest for: Organizations with an identified use case ready to prove out with real data and real users.
We design, build, and deploy a working AI pilot integrated with your actual data and systems. You will have functional software running in a controlled environment, with an evaluation harness measuring accuracy against the KPIs we agreed on in discovery.
What you get:
- A working pilot system connected to your data sources and integrated with relevant enterprise systems (CRM, databases, APIs)
- A custom evaluation harness measuring accuracy, reliability, and business impact against agreed KPIs
- Human-in-the-loop workflows where appropriate, so the system is safe for real-world use from day one
- A go/no-go assessment with data to support the decision to scale or pivot
Why it matters: Most AI pilots never reach production. The most common reason is not that the AI did not work—it is that the pilot was built as a throwaway prototype with no path to production. Our pilots are built with production standards from the start: security, access control, monitoring, and integration architecture that scales.
Typical outcome: A validated, working system and a clear-eyed assessment of whether to proceed to full deployment—along with the technical foundation to do so.
Production Implementation
3–6+ monthsBest for: Organizations scaling a proven pilot to enterprise-wide deployment.
We take a validated pilot and harden it for production: full security implementation, deep integration with enterprise systems, performance optimization, user training, and structured handoff to your team.
What you get:
- Production-grade system with enterprise security (encryption, access control, audit logging)
- Deep integration with your IT landscape—SSO, databases, APIs, legacy systems, compliance infrastructure
- Monitoring and alerting for model performance, system health, and data drift
- Comprehensive documentation, deployment runbooks, and training for your operations team
- A managed handoff process designed for your team's self-sufficiency
Why it matters: The gap between “working pilot” and “production system” is where most AI initiatives die. It requires a different set of skills than building the initial model—security architecture, enterprise integration, change management, and operational hardening. Our team has navigated this transition inside banking infrastructure, pharmaceutical operations, and telecom environments where failure is not an option.
Typical outcome: A fully operational AI system running in your environment, with your team trained and equipped to manage it independently.
Optional: Managed AI Service
For teams that want ongoing expert oversight after launch.
Some clients prefer to retain our team for continuous monitoring, periodic model updates, and on-call support after the initial deployment. This is entirely optional—we design every system for handoff—but for organizations that want the assurance of expert oversight, we offer a flexible retainer.
Includes:
- Quarterly model performance reviews and recalibration
- Proactive monitoring for data drift and system health
- Priority access to our team for enhancements or troubleshooting
- Ongoing alignment with evolving compliance requirements
This is a safety net, not a dependency. You can step into full self-management at any time.
Technical Capabilities
What we build with—and what we build into.
Natural Language Processing & Generative AI
We build on GPT-4o, Claude, Llama, and Mistral—selecting the right model for your latency, cost, and data-residency constraints. Orchestration layers use LangChain or LlamaIndex with structured output parsing, token-budget optimization, and prompt versioning tracked in Git. Every system ships with output guardrails (PII redaction, hallucination detection, schema validation) tuned to your domain vocabulary and compliance requirements.
Retrieval-Augmented Generation (RAG)
Hybrid retrieval pipelines combining dense embeddings (OpenAI, Cohere, open-source bi-encoders) with BM25 keyword search, backed by pgvector, Pinecone, or Qdrant depending on your infrastructure. We tune chunk boundaries and overlap windows per document type, apply cross-encoder re-ranking for precision, and build citation tracing so every generated answer links back to the source paragraph and page number.
Predictive Analytics & Machine Learning
XGBoost, LightGBM, and PyTorch pipelines selected by benchmarking against your actual data—not synthetic samples. Feature engineering built on domain-specific signals, with SHAP-based explainability for stakeholder buy-in and regulatory review. Models deploy behind feature stores with automated drift detection (PSI, KS tests) and scheduled retraining triggers when performance degrades past defined thresholds.
Enterprise Integration
Kafka and RabbitMQ event-driven pipelines, REST/GraphQL API layers, and adapters for legacy systems—SOAP services, mainframe extracts, FTP batch feeds. Infrastructure as code with Terraform or Pulumi on AWS, Azure, GCP, or on-premise. SSO via SAML/OIDC, secrets management through Vault or cloud-native KMS, and API gateways with rate limiting, auth, and observability built in from day one.
MLOps & Model Governance
Model lifecycle managed through MLflow or Weights & Biases: experiment tracking, artifact versioning, and reproducible training runs. Data versioning with DVC, model serving on Kubernetes with canary and blue-green deployment strategies, and rollback automation. For regulated environments, full lineage tracking—from raw training data through feature transforms to production predictions—with audit-ready export for compliance review.
Evaluation & Quality Assurance
Domain-specific evaluation harnesses running in CI: deterministic test suites, LLM-as-judge with calibrated rubrics, and human evaluation protocols with inter-annotator agreement tracking. Regression benchmarks gate every deployment. For generative systems, we measure factual accuracy, citation fidelity, and hallucination rates against golden datasets maintained by your subject-matter experts.
Industry Experience
We have delivered AI systems in environments where the stakes are high and the requirements are non-negotiable.
Financial Services
Trade document extraction and classification, KYC workflow automation, regulatory report generation, and research tools that surface relevant precedents across thousands of filings. Built to pass vendor security assessments and integrate with core banking infrastructure.
Pharmaceutical & Healthcare
Clinical trial data reconciliation across disparate source systems, adverse event signal detection, medical literature review acceleration, and submission-supporting pipeline automation. Designed for HIPAA compliance and FDA validation requirements from day one.
Telecommunications
Customer intent classification at scale, network anomaly detection, churn prediction, and service automation that resolves issues rather than deflecting them. Our work in this space handles hundreds of thousands of daily interactions in production.
Insurance
Claims triage and routing, policy document analysis, underwriting risk assessment, and loss ratio optimization. Systems built for high-volume, accuracy-critical workflows where a missed detail has real financial consequences.
Problem first. Technology second. Production always.
Every engagement follows the same core principles, adapted to your context:
- Start with the business problem. We do not pitch technology looking for a use case. We understand what would move the needle for your organization, then determine whether and how AI can help.
- Build for production from day one. Even in a pilot, we architect for security, integration, and scale. No throwaway prototypes that need to be rebuilt.
- Test rigorously, iterate openly. We build evaluation harnesses specific to your use case, share results transparently, and course-correct based on data—not optimism.
- Keep humans in the loop. We design systems that augment your team's judgment, not replace it. AI handles the heavy lifting; humans handle the decisions that require context, nuance, or accountability.
- Hand off cleanly. Documentation, training, source code, and operational runbooks. Our success is measured by your ability to run the system without us.
How We Work: Three Phases, One Accountable Team
- Phase 1
Discover
2–4 weeksIdentify the highest-impact AI opportunity and validate the business case.
- Phase 2
Pilot
6–10 weeksBuild a working system integrated with your real data and infrastructure.
- Phase 3
Scale
3–6+ monthsHarden, deploy enterprise-wide, and hand off to your team.
Ready to explore what AI can do for your organization?
Start with a 30-minute conversation focused on your challenge, not our capabilities.