The weave
Most consultancies sell one thread — the deck, or the code, or the course. Chevabot runs the whole loom, because the gap between a strategy and a running system is where AI initiatives fray.
Strategy & Advisory
Chart the pattern before the first thread.
Most AI programs unravel at the framing. We map what AI is actually worth inside your operation — readiness, opportunity, governance — and hand your board a pattern it can fund with confidence.
Think of this as a technical due diligence on your own business. We interview stakeholders, audit your data estate, review your vendor shortlist, and model the cost-to-value curve for each use case. The output is not a strategy deck — it is a ranked backlog with go/no-go gates, risk registers, and a 90-day execution plan. Boards get a funding narrative; operators get a roadmap they can actually build against.
- AI readiness assessment
- Opportunity map & prioritized roadmap
- Executive advisory & governance
- Vendor, platform & model evaluation
Custom Implementation
Built thread by thread, in your environment.
Conversational assistants, autonomous agents, automation, machine-learning pipelines — designed for the systems you already run and for the region's data-residency realities, not for a demo.
We do not drop a chatbot on your website and call it done. Our teams wire language models into your identity provider, vectorize documents into a retrieval store, expose tools via Model Context Protocol servers, and write regression tests for prompt chains. The result is software that fits your SDLC, passes your security review, and keeps working after the launch announcement.
- Conversational AI & assistants
- Autonomous agents & workflow automation
- Machine-learning & data pipelines
- Integration with existing systems
Training & Enablement
Hands that know the loom.
A system nobody trusts is a system nobody uses. We train the executives who govern it, the teams who work beside it, and the specialists who will extend it — on your real workflows, in plain language.
Adoption is the real KPI. We run executive briefings that translate token economics into board questions, team workshops that let non-technical staff operate an AI assistant safely, and practitioner tracks that teach prompt engineering, evaluation frameworks, and retrieval architecture. Every session uses your data, your workflows, and your vocabulary.
- Executive briefings
- Hands-on team workshops
- Organization-wide AI literacy
- Practitioner upskilling tracks
Managed Operations
We keep the shuttle moving.
Models drift, costs creep, patterns fray. Under a managed retainer we monitor, evaluate, and improve your AI systems continuously — ours or the ones you already have — so they stay an asset.
Production AI decays. Models drift, APIs change price tiers, and user behavior invalidates assumptions. Under retainer we monitor latency, cost, accuracy, and safety metrics; run regression suites; manage model updates; and report monthly on value delivered. You get an operations rhythm without building an internal MLOps team.
- Monitoring, evaluation & reliability
- Model updates & regression testing
- Cost & performance optimization
- Retainer support & reporting
Specialist threads
Named capabilities on the shelf, woven into any engagement — from a single deployment to a full program.
- Thread 01 · Private
Local & private AI deployment
On-premise and personal AI deployments tuned to your industry and business — models running inside your walls, for operations whose data cannot leave the building.
For banks, healthcare, and government entities, 'cloud-only' is not an option. We deploy open-weight models on your servers or air-gapped appliances, configure local inference stacks, and tune them to your compliance boundaries. Sensitive data never leaves your network, yet users still get conversational search, summarization, and coding assistance.
- Thread 02 · Knowledge
RAG pipelines on your data
Retrieval-augmented generation pipelines that ground local and online models in your own documents, systems, and terminology — so answers come from your knowledge, not the internet's.
Retrieval-Augmented Generation grounds answers in your documents instead of the model's training memory. We build ingestion pipelines that chunk, embed, and index your knowledge base; design hybrid search (semantic + keyword) so answers cite sources; and add guardrails so the assistant says 'I do not know' instead of hallucinating.
- Thread 03 · Integration
MCP server creation & deployment
Custom Model Context Protocol servers that connect AI assistants to your tools, databases, and workflows — built and deployed for your specific needs.
The Model Context Protocol is the USB-C for AI tools. We build MCP servers that let an assistant read your CRM, query your database, or trigger your workflows through a secure, schema-defined interface. Your existing systems do not need to be rewritten — they just need a clean adapter.
- Thread 04 · Efficiency
Token optimization
Tooling and instrumentation that cut token burn on online models — lowering API and subscription costs without lowering the quality of the answers.
Online model bills scale with prompt length and call frequency. We instrument your prompts, cache repeated context, compress conversation history, and route requests to the smallest model that can handle them. Typical results are lower API spend with unchanged output quality — the engineering is invisible, the budget impact is not.
- Thread 05 · Right-sized
Tiny & small language models
TLM and SLM deployments for small and medium businesses and industries — the capability you actually need, at a fraction of the cost and footprint of frontier models.
Not every job needs a frontier model. We fit task-specific TLMs and SLMs — models with hundreds of millions to a few billion parameters — to narrow use cases like classification, extraction, or form validation. They run cheaply, privately, and sometimes entirely on device.