AI Solutions & Automations
We implement AI chatbots, RAG pipelines, process automation, internal copilots, and system integrations — delivering measurable ROI from week one. No demos that never reach production.
Who this is for
CTOs, product leads, and COOs who want measurable ROI from AI — not a pilot that never goes to production. You need integrations with existing systems, not standalone AI toys.
Ready to get started?
Book a free 30-minute scoping call with a senior engineer.
What You Get
Every engagement includes these deliverables — not optional extras, not dependent on tier.
- AI solution scoping and feasibility assessment in Week 1
- Production-grade implementation with defined accuracy thresholds
- Integration with your existing systems (CRM, ERP, ticketing, docs)
- RAG pipelines with tested retrieval accuracy metrics
- Monitoring and retraining procedures for model drift detection
- User acceptance testing with agreed accuracy baselines
- Full documentation and internal training for your team
- ROI measurement framework: baseline vs. post-deployment tracking
How We Deliver
A structured, phase-based delivery process — you know what happens next at every stage.
Use Case Workshop
Week 1
Identify high-ROI automation candidates with payback period estimates. We map every manual workflow that costs more than 4 hours per week and rank by ROI, complexity, and risk.
Deliverables
- Automation opportunity ranking
- ROI estimates
- Complexity ratings
- Risk flags
Feasibility Assessment
Week 1–2
Evaluate data quality, integration complexity, accuracy requirements, and risk level. Some ideas are fast wins — some need groundwork first. We tell you which is which, in writing.
Deliverables
- Feasibility report
- Data quality assessment
- Integration complexity map
- Build vs. defer recommendation
Design & Prototype
Weeks 2–4
Architecture for data pipelines, LLM orchestration, integration points, and human-in-the-loop escalation. Prototype validated against real data samples before full build.
Deliverables
- Solution architecture
- Integration design
- Prototype with sample data
- Approval to build
Build & Test
Weeks 3–8 (varies)
Build the pipeline, connect the integrations, define accuracy thresholds, run acceptance testing. We define what "working" means before we start and test against it.
Deliverables
- Working AI system
- Accuracy benchmark report
- Integration test results
- UAT sign-off
Monitor & Improve
Post-launch
Every AI system includes monitoring: accuracy tracking, latency metrics, error rate dashboards, and drift alerts. We track whether the automation is still working — and fix it when it is not.
Deliverables
- Monitoring dashboards
- Drift alert configuration
- Monthly accuracy reports
- Retraining schedule
Engagement Models
Choose the model that fits your goals and timeline. We can also mix models within a single engagement.
AI Assessment (Fixed)
A 2-week engagement to identify your top 5 automation opportunities with estimated ROI, complexity, and integration requirements. Delivered as a written report and presentation.
Project Build
Scoped implementation of 1–2 AI solutions. Milestone-based billing. Includes integration, testing, documentation, and 30-day post-launch monitoring.
Ongoing AI Partner
Monthly retainer for continuous AI and automation iteration — new use cases, model tuning, integration expansions, and performance monitoring.
Common Pitfalls We Prevent
These are the issues we see repeatedly when clients come to us after working with other vendors. We build processes that prevent them before they happen.
Pilots that never reach production
We define production-readiness criteria at the start, not after the demo. Done means deployed, monitored, and running — not presented in a meeting.
AI systems without monitoring
Every deployment includes accuracy tracking and drift alerts. Models degrade over time — we alert before users notice.
Data quality ignored until launch
We assess and document data quality risks in Week 1. Poor data quality is the top reason AI projects fail — we address it before writing a line of code.
Overpromised accuracy
We set realistic accuracy baselines with stakeholders before build starts. AI is not 100% accurate — we agree on acceptable thresholds upfront and build to them.
Vendor lock-in through proprietary AI
We build with open standards and document all prompts, pipelines, and models used. You can switch providers or self-host without losing your investment.
Frequently Asked Questions
It depends on the use case. RAG-based knowledge assistants work on existing documents — no training data needed. ML models and fine-tuned LLMs require more data. We assess your data situation in Week 1 and recommend the right approach.
OpenAI (GPT-4o, o1), Anthropic (Claude), Google (Gemini), and open-source models (Llama, Mistral). We select the model based on your accuracy, cost, latency, and privacy requirements — not vendor preference.
We never include PII or sensitive data in prompts without explicit consent and appropriate safeguards. For high-sensitivity use cases we offer private model deployment (self-hosted or VPC-isolated). Audit logging on every AI call is standard.
Retrieval-Augmented Generation connects a language model to your knowledge base — documents, wikis, databases — so it answers questions with your actual data, not just its training data. We use RAG for internal knowledge assistants, customer support bots, and any use case where the model needs to reference your specific information.
Talk to an Expert — AI & Automation
Book a free 30-minute scoping call with a senior engineer — no sales pitch, just a real conversation about what you need.