An accountable AI development partner
We take on a defined part of your AI roadmap, from data and models to deployment and governance, and own the outcome. Each stage is measured against criteria agreed up front and documented as we go.
How a development partnership differs from AI staffing
With AI staffing you add people to your team and manage them yourself. With a development partnership we own outcomes: a cross-functional team with its own lead, delivery against agreed milestones and evaluation criteria, and governance work built in from the start. You keep the product decisions.
It suits organisations that want AI in production but don’t want to build and manage a full AI team in-house, or can’t do it fast enough.
AI delivery with a paper trail
We work as your long-term AI development partner: strategy, data, models, deployment and governance, delivered by one accountable team and documented as we go.
One team from idea to production
Strategy, design, build and iteration sit with the same team, so nothing gets lost between vendors.
Working inside your rhythm
Our engineers, data scientists and product people follow your backlog, ceremonies and service levels.
Ready for governance reviews
Monitoring, documentation and guardrails go in from day one, which makes internal risk, security and compliance reviews far easier.
Support across the AI lifecycle
From clean data to reliable deployments, so your people can spend their time on product and business decisions.
Data strategy and collection
What data you need, where it lives and how to capture it responsibly: schemas, integration plans and labeling workflows.
Data labeling and preparation
Structured guidelines and QA to build training and evaluation sets across text, images, audio and tabular data.
Feature engineering and model development
Classical ML combined with LLMs and retrieval pipelines, fitted to your domain, constraints and latency needs.
MLOps, deployment and monitoring
CI/CD, observability and rollback for models and agents, so they run safely in production.
Optimisation and model governance
Ongoing checks on performance, drift and risk, with guardrails, documentation and evaluation harnesses that match your governance standards.
AI technologies and capabilities
Modern AI tooling on top of plain, maintainable engineering.
On the engineering side we work with React, Next.js, TypeScript, Node.js, Python, Supabase, Google Cloud and AWS, so the AI layer fits your existing stack and data sources.
AI development partnership: frequently asked questions
Why work with Dignep on AI?
Because we own outcomes, not hours: evaluation criteria agreed up front, decisions written down, and one team from data to deployment. We aim for systems that are reliable and explainable in daily use, not just in a demo.
Which AI technologies do you work with?
LLM stacks, retrieval systems, classical ML models and the infrastructure around them: APIs, data pipelines, monitoring and deployment, on open-source and commercial platforms.
How is this different from AI staffing?
AI staffing adds individual specialists that you manage. A partnership means we bring the structure, processes and a multi-disciplinary team, and own a significant part of the AI roadmap with you.
How do we start?
A free 30-minute discovery call, a written proposal within two working days, then usually a discovery or PoC phase before we agree a longer-term model.
What does working with an AI development partner look like?
You agree the outcome and the evidence that will prove it; we put a team on it and report against both. Most partnerships start with a short discovery or a 4–8 week proof of concept, then grow into an MVP and a standing team. Every stage leaves documentation behind: evaluation results, decisions, risks and changes.
Start small
A short discovery or a 4–8 week proof of concept before any long commitment.
Grow to a standing team
A dedicated team of 3–15+ engineers, set up in 2–4 weeks, on 30 days' notice.
On the record
Evaluation results, decisions, risks and changes, kept as we go.
How a partnership usually unfolds
Nobody should sign up for a year of AI work on the strength of a sales call. Each step earns the next one.
After the first call you get a written proposal within two working days.
Discovery call
Thirty minutes, free. The business problem, the data you have, the constraints, and whether AI is the right tool at all.
Written proposal (within 2 working days)
The first phase, its success criteria, team, schedule and pricing.
Discovery or PoC (4–8 weeks)
A focused build on your real data, measured against agreed criteria. Go, change course or stop.
MVP and first users
A working product with logging, monitoring and an evaluation set that is rerun after every change.
Standing team (2–4 weeks to set up)
A dedicated team of 3–15+ engineers carries the roadmap, with governance reviews on a fixed cadence and 30 days' notice.
Partnership, staffing or a one-off project?
| Development partnership | AI staffing | One-off project | |
|---|---|---|---|
| Who owns the outcome | We do, against agreed milestones and evaluation criteria | You do; we supply people | We do, for one fixed scope |
| Team | Cross-functional, with its own lead; 3–15+ engineers as it grows | 1–5 specialists in your team | Sized to the project |
| Start time | Discovery or a 4–8 week PoC, then 2–4 weeks to stand up a larger team | 5–10 working days | 1–3 week discovery |
| Governance work | Built in: documentation, evaluation, monitoring, risk review | Follows your processes | Agreed in the scope |
| Ends | 30 days' written notice for team-based work | 30 days' written notice | On milestone acceptance |
Pricing is scoped and quoted after a free 30-minute discovery call, with a written proposal within two working days.
Kept, not written up later
Evaluation results, architecture decisions, change records and a risk log, kept under our ISO/IEC 20000-1 certified processes rather than written up at the end.
Governance in the same team
We work with ISO 27001, SOC 2, the EU AI Act, NIST AI RMF and Nepal Rastra Bank AI guidance. See AI governance and security.
Exit terms in writing
Team-based work runs on 30 days' written notice; projects end on milestone acceptance. Both are in the proposal.
Specific services inside a partnership: AI/ML engineering, generative AI, AI agents, data engineering and MLOps. All of them are listed on our services page.
When this isn't the right fit
- You want to hire and manage AI specialists yourself. AI staffing is simpler and cheaper.
- You need a demo for next month's board meeting and nothing after. A partnership is built for production, not slides.
- Nobody on your side can make product decisions. We can own delivery, but not your priorities.
Certifyi AI SaaS GRC platform
A product we took from architecture to deployment:
We built Certifyi, an AI SaaS platform for governance, risk and compliance, from product architecture to deployment: Go, Next.js and PostgreSQL on AWS, with CI/CD in GitHub Actions. It maps 100+ compliance frameworks in one system and automates evidence collection. The case study reports up to 3x less manual compliance work for its users.
Further reading
Plan your AI roadmap with us
Book a free 30-minute call. You get a written proposal within two working days.
