AI engineering services that end in a working production system
Scoping, architecture, build, deployment, monitoring and handover under one engagement. Eleven service lines covering retrieval, agents, LLM products, machine learning, cloud infrastructure and process automation, all delivered by the engineer who designs them.
Six things we do at a level most teams cannot match
We are not generalists or prompt writers. We are AI engineers who have put production systems in front of real users across six technical practice areas.
RAG systems that retrieve the right passage
Most RAG projects fail at the retrieval layer, not the model. We build with hybrid search (BM25 plus dense vectors), cross-encoder reranking, citation grounding, and RAGAS evaluation against ground-truth benchmarks before anything goes live. Your knowledge base becomes searchable in seconds, with sources attached.
AI agents that complete real tasks
Not chatbots. Agents that connect to your APIs, databases, and tools, then take action and report back. We build on LangGraph and CrewAI with human approval gates, retry logic, and full trace observability, so you always know what the agent did and why it did it.
Workflow automation that runs around the clock
We self-host n8n inside your own infrastructure, so your data never leaves the building. One platform, 400 plus integrations, AI decision logic baked into every workflow. No per-operation pricing, no vendor lock-in, no surprise downtime.
LLM products with a real backend
Full AI products from scratch. Backend APIs, authentication, multi-tenancy, usage tracking, dashboards, database design, and cloud deployment, all in one engagement. You ship a product in weeks, not quarters, and you own the whole stack at the end.
MLOps built for regulated environments
Full-lifecycle ML infrastructure: training pipelines, model registries, shadow deployment, statistical drift detection, A/B testing, and CI/CD. Certified on AWS SageMaker, Azure ML, and GCP Vertex AI, with audit-ready lineage from the first commit.
Fine-tuning with ground-truth validation
We fine-tune domain-specific models on your proprietary data using QLoRA and DPO alignment. Every model is validated against held-out benchmarks with ROUGE, BERTScore, and citation accuracy before it touches a single production request.
Eleven services, one accountable engineering partner
From scoping through deployment and support. One partner, full accountability, no vendor juggling.
RAG and Document Intelligence
Search, understand, and answer from PDFs, databases, CRMs, contracts, support tickets, and internal wikis. Hybrid retrieval, reranking, citation grounding, access control, and a production evaluation pipeline come standard.
AI Agents and Task Automation
Agents that draft emails, review documents, qualify leads, run compliance checks, and handle internal operations, connected directly to your CRM, databases, and APIs. Full audit trails and human approval where the stakes are high.
LLM SaaS and Internal Copilots
We build the whole product. Backend APIs, user authentication, admin dashboards, usage analytics, database schema, and cloud deployment. Copilots for support, sales, HR, legal, and operations teams that your staff will keep using.
Machine Learning Systems
Forecasting, fraud detection, recommendation engines, and predictive analytics, with evaluation frameworks, drift monitoring, and retraining pipelines built in. Systems that get better over time instead of quietly going stale.
Cloud AI and MLOps Infrastructure
Production-ready ML infrastructure on AWS, Azure, and GCP. Automated CI/CD, model registries, shadow deployment, drift alerts, and cost optimization. Triple-certified, with zero-downtime deployments as the baseline.
AI Strategy and Architecture Review
Not sure whether to build or buy? Which model fits your use case? We map your workflow, design the architecture, model the ROI, and hand you a phased plan you can act on. No fluff, just a technical plan that survives contact with reality.
Intelligent Process Automation
Combines RPA, AI, and NLP to automate the messy workflows that rule-based bots break on. Document processing, compliance checks, data extraction, and multi-system orchestration, all without disrupting your live operations.
LLM Fine-Tuning and Alignment
Domain-specific fine-tuning on your proprietary data using QLoRA, LoRA, and DPO alignment. Production evaluation pipelines with ROUGE, BERTScore, and human preference scoring. Every model is validated before deployment, not after.
Data Engineering and Analytics
Data pipelines, warehouse integrations, and BI dashboards that feed your AI systems clean, structured data. Snowflake, Databricks, BigQuery, and real-time streaming with Kafka and Airflow, built to scale with you.
Automation that reasons, not just clicks
Standard RPA follows fixed rules. Our IPA layer adds AI reasoning, NLP, and computer vision to handle the variable, judgment-heavy work that conventional bots always break on.
End-to-End Process Automation
We map your highest-cost manual workflows, find the automation opportunities that matter, and build systems that remove the work entirely. Not faster humans. No human needed for these tasks at all.
IPA Managed Services
We run your automation infrastructure for you. Round-the-clock monitoring, proactive health checks, incident response inside two hours, and monthly optimization reviews. Your bots keep running while your team stays focused.
Team Augmentation
Need AI engineers inside your team? We embed automation specialists, RPA developers, and AI architects directly into your delivery workflow. Enterprise-grade talent, no hiring cycle, no overhead.
Custom IPA Solutions
Built around your exact workflow constraints, tech stack, and compliance requirements. Includes full system integration, team training, documentation, and a 90-day transition roadmap so your team owns it cleanly.
Detailed guides to our four core practice areas
How each system is architected, what it costs to run, where these projects usually fail and what we do differently.
RAG system development
Hybrid retrieval, cross-encoder reranking, citation grounding and RAGAS evaluation against ground-truth benchmarks before launch.
Practice areaAI agent development
LangGraph and CrewAI agents with tool access, approval gates, retry logic and full trace observability on every run.
Practice areaLLM SaaS and copilots
Complete AI products: backend APIs, authentication, multi-tenancy, usage metering, dashboards and cloud deployment.
Practice areaMLOps and LLMOps
Training pipelines, model registries, shadow deployment, statistical drift detection and audit-ready lineage.
A delivery process built for production, not demos
Every engagement follows the same disciplined path. You see real output early, and you always know where the project stands.
Scope and KPIs
A free call to understand the workflow, the data, and the constraints. We define what success looks like in measurable terms before anyone writes code.
Architecture and Plan
A documented system design, model selection, cost model, and phased delivery plan. You approve the approach and the budget before we start building.
Build and Demo
Agile sprints with a working demo from sprint two onward. Weekly updates, honest reporting on blockers, and evaluation against the KPIs we set.
Deploy and Hand Off
Cloud deployment, monitoring, full documentation, and a clean handoff. Then we stay on for support, model tuning, and quarterly improvements.
AWS and GCP are where most of this work lands
Those two carry the majority of our production deployments, and they are where our deepest operational experience sits. Azure is used where a client is already committed to it, and we are certified on all three so the choice is yours rather than ours.
No demos. No notebooks. Only working systems.
One partner, full accountability
Architecture through deployment through support. No vendor coordination, no accountability gaps. You have one person to call.
Production-ready from the first sprint
Every system is tested against defined success criteria before it goes live. Clean architecture, documented handoff, real monitoring from day one.
KPIs before code
We define what success looks like before writing a line. Efficiency gains, cost reductions, retrieval accuracy. We track them throughout.
You see progress every week
Working demos from sprint two, structured updates, and honest communication about blockers. No surprises at handoff.
Sometimes the honest answer is that you do not need a large language model
Most AI vendors have one hammer. We have shipped enough production systems to know that a generative model is the wrong tool at least as often as it is the right one, and we will say so on the first call rather than three months into a build.
Where generative AI quietly loses
Structured tabular prediction, demand forecasting, credit and fraud scoring, anomaly detection and anything that needs a stable, explainable decision boundary. A gradient boosted model on clean features usually beats an LLM on accuracy, latency and cost, and it passes an audit far more easily.
| Your problem | What we would recommend |
|---|---|
| Answering questions over documents | RAG with hybrid retrieval and citations |
| Predicting churn, demand or risk | Classical ML on your tabular data |
| Multi-step work across your tools | An agent with approval gates |
| Extracting fields from fixed forms | Document AI, not an LLM call per page |
| Deterministic rules and thresholds | Plain software, no model at all |
| Domain tone and vocabulary at scale | Fine-tuning with held-out evaluation |
Every scoping call ends with a written recommendation, including the option of not building anything.
Questions about scope and engagement
How service lines map to a real engagement.
Do you take on a single service or the whole build?
Both. Some clients bring a scoped problem such as a RAG system over a document set. Others hand over the whole product including backend, authentication, dashboards, deployment and monitoring. The engagement model on the pricing page maps to each.
Can you work alongside our existing engineering team?
Yes. On retainer and team augmentation engagements we embed with your developers, use your repositories and review process, and hand over documented architecture as we go rather than at the end.
What if we are not sure which service we need?
Start with an architecture review. We map the workflow, the data and the constraints, then give you a written recommendation with a phased plan. If the answer is that classical machine learning or plain software solves it more cheaply, that is what the document says.
Tell us the workflow that is costing you money
Thirty minutes on a call is usually enough to tell you which of these services fits, what the architecture looks like and roughly what it costs.