01
AI Agents & Automation
Agents that hold up under real traffic: scoped tools, guarded actions, measurable reliability.
- Custom AI agents
- Agentic workflows
- Multi-agent systems
- Tool and API integration
- Human-in-the-loop workflows
AI engineering & consulting
Build AI that works in production.
We design, build, secure and optimize AI agents, document intelligence systems and custom LLM/SLM solutions for real-world business workflows.
The engineering loop
- 01BuildProduction systems, not demos: agents, document pipelines and retrieval that run daily.
- 02AdaptFine-tuning and domain adaptation so models handle your data and terminology.
- 03OptimizeLatency, throughput and cost engineered down without losing accuracy.
- 04SecureRed teaming, guardrails and observability that satisfy security review.
AI agentsDocument intelligenceRAG systemsModel fine-tuningRLHF & DPO alignmentSynthetic data generationVoice AI agentsInference optimizationModel routingRed teamingGuardrailsAI observabilityCost per taskAI agentsDocument intelligenceRAG systemsModel fine-tuningRLHF & DPO alignmentSynthetic data generationVoice AI agentsInference optimizationModel routingRed teamingGuardrailsAI observabilityCost per task
Clients
Who We Work With
We work with startups, technology companies and enterprises looking to build, scale or improve real-world AI systems.
Startups & AI Product Companies
Companies building AI-powered products that need specialized AI engineering expertise.
Enterprise Teams
Organizations with existing technology teams that need additional expertise in AI agents, document intelligence, LLM/SLM engineering, optimization or AI security.
Businesses Adopting AI
Companies looking to identify and implement practical, high-value AI use cases across their business processes.
What we do
Practical AI engineering, not generic software development.
From AI prototype to reliable, secure and cost-efficient production. We take responsibility for the parts that decide whether an AI system survives real usage: architecture, evaluation, security and cost.
- Architecture that fits your data and constraints
- Working systems, shipped incrementally
- Measured quality instead of vibes
- Security review before production
- Cost and latency treated as requirements
- Handover your team can maintain
Services
Six engineering pillars
Each pillar is a discipline we own end to end, engaged individually or as a full build.
02
Intelligent Document Processing
High-accuracy extraction and reasoning over invoices, claims, contracts and forms.
- Invoice processing
- Claims processing
- Contract and document analysis
- Forms and report extraction
- OCR and document understanding
03
LLM & SLM Engineering
Retrieval, adaptation, fine-tuning and alignment that make models fit your domain and your data.
- RAG systems
- Domain-specific AI
- Model fine-tuning (LLM & SLM)
- Supervised fine-tuning (SFT)
- LoRA / QLoRA / PEFT
04
AI Infrastructure & Optimization
Serve models faster and cheaper: quantization, routing, caching, throughput engineering.
- Inference optimization
- Model quantization
- GPU/CPU optimization
- Latency optimization
- High-throughput inference
05
AI Security, Evaluation & Observability
Adversarial testing, guardrails and observability so AI can be trusted in production.
- AI red teaming
- Prompt injection protection
- Agent security
- Guardrails
- PII and data protection
06
Voice AI Agents
Real-time voice agents that answer, qualify and resolve calls with low latency and clean handoffs.
- Inbound and outbound voice agents
- Real-time speech-to-text and text-to-speech
- Low-latency streaming pipelines
- Barge-in and turn-taking control
- Telephony integration (SIP, Twilio)
How we help
Discover → Build → Evaluate → Secure → Optimize → Scale
We can join at any stage of an AI project, from architecture and proof-of-concept through production deployment, optimization and ongoing operation.
- 01
Discover
Map the workflow, the data and the constraints. Decide what is worth building.
- 02
Build
Architecture, agents, retrieval and integrations shipped as working software.
- 03
Evaluate
Task-level test sets and scoring, so quality is a number rather than an impression.
- 04
Secure
Red teaming, guardrails, permissioning and data protection before launch.
- 05
Optimize
Latency, throughput and cost per task tuned against production traffic.
- 06
Scale
Monitoring, routing and operational ownership as usage grows.
AI engineering approach
How we keep AI systems reliable
The same working rules apply whether we are building an agent, a document pipeline or a fine-tuned model.
Evaluation before scale
Nothing ships without a task-level test set. Improvements are proven against it, not argued about.
Smallest model that clears the bar
We start from the quality target and work down to the cheapest, fastest model that meets it.
Failure modes are designed
Retries, fallbacks, confidence thresholds and human review are part of the architecture.
Security is not a later phase
Tool permissions, injection defenses and PII handling are set while the system is being built.
Cost is an engineering metric
Cost per task is tracked alongside latency and accuracy from the first prototype.
Your team owns the result
Documented architecture, handover and code your engineers can maintain without us.
Why us
The hard part is everything after the prototype.
Most teams can get a demo working. Accuracy on messy data, security review, latency under load and a cost per task a CFO accepts. That is the work we specialize in.
- Evaluated
- Task-level test sets score every change before it ships.
- Adapted
- Fine-tuning, alignment and retrieval tuned to your data and terminology.
- Optimized
- Latency, throughput and cost engineered down without losing accuracy.
- Secured
- Red teaming, guardrails and observability that pass security review.
Case studies / results
Representative engagements
Anonymized summaries of the kind of work we deliver. Full references available on request under NDA.
Invoice and claims intake
Straight-through processing
A document pipeline with classification, extraction and validation, routing only low-confidence cases to reviewers. Manual handling drops to exceptions.
LLM cost optimization
Lower cost per task
Model routing, caching and a fine-tuned small model for the highest-volume step, benchmarked against the original quality bar.
Inference optimization
Faster responses
Quantization, batching and serving changes applied against production traffic profiles to cut tail latency.
Agent red teaming
Security sign-off
Adversarial testing of tool use and prompt injection paths, followed by guardrails and monitoring, so an agent could go live.
AI advisory
AI Advisory & Consulting
Not sure how to approach an AI project? Start with a single conversation. We review your idea or existing system and give you a straight technical read on feasibility, architecture, risk and cost.
- 1:1 AI architecture consultation
- AI strategy and roadmap
- AI architecture review
- Existing AI system assessment
- Build-vs-buy decisions
- Technical due diligence
- Production readiness assessment
- Fractional AI/CTO advisory
Next step
Have an AI problem to solve? Let's talk.
Bring the workflow, the constraints and the data. We will tell you what is realistic, what it costs and how we would build it.