Service · Forward Deployed Engineering
Forward Deployed AI Engineers
Senior AI engineers, embedded in your business until it works.
Octobit8's forward deployed engineers (FDEs) work inside your team, your systems and your constraints to take AI from idea to production and adoption. Not slides. Not a hand-off. Shipped software.
Forward deployed engineering is a model pioneered by leading AI and data companies: instead of selling a tool and leaving, you send engineers to sit with the customer and solve the real problem in the real environment. An FDE is part software engineer, part solutions architect, part product thinker. They learn your workflow, talk to the people who do the work, build against your actual data and systems, and stay until the solution is used every day.
01 / Fit
Who is this for
Common profiles that get the most out of this service.
- 01
You've bought an AI platform nobody has turned into a product
Model access or an AI platform sitting unused because nobody has the bandwidth to build against it.
- 02
Your pilot stalled at security review or adoption
It worked in a sandbox and stopped at integration, security review or getting users to actually use it.
- 03
Your team knows the business, not LLMs
Deep domain knowledge in-house, but no one with hands-on agent, RAG or evaluation experience.
- 04
Your data lives in legacy systems
Spreadsheets, on-premise servers and systems with no API that off-the-shelf tools can’t reach.
- 05
You need to move fast without hiring a full AI team
A senior engineer or pod now, without a multi-month hiring process first.
02 / Principles
What sets it apart
Principles we apply on every engagement—so results are measurable, not just delivered.
Discovery on the ground
Shadow users, map workflows and find the use case with the clearest return.
Build in your environment
Write production code inside your cloud, repositories and CI/CD, following your security and coding standards.
Integrate with everything
Connect AI to ERPs, EHRs, booking engines, LMSs, data warehouses and legacy systems.
Own reliability
Set up evaluation, monitoring, alerting and cost controls.
Drive adoption
Train users, collect feedback and iterate weekly until usage sticks.
Transfer knowledge
Pair with your engineers and leave behind documentation, runbooks and a team that can run it.
03 / Scope
Capabilities & deliverables
Concrete workstreams we plan, execute, and hand over with documentation and dashboards.
FDE Sprint
1 FDE, 4–6 weeks — one focused use case taken to a production pilot.
- Week 1 immersion and data review
- Working prototype by week 3
- Production roadmap by week 4
FDE Pod
2–3 FDEs plus a part-time architect, 3–6 months — a multi-use-case AI programme.
- Coordinated delivery across use cases
- Shared architecture and evaluation standards
- Ongoing stakeholder reporting
Embedded FDE
1–2 FDEs, 6–12 months, renewable — ongoing AI capability while you build your own team.
- Continuous roadmap execution
- Pairing and mentoring your engineers
- Renewable month to month
04 / Deliverables
What you get
Concrete deliverables from this engagement.
A working solution in production, used daily by your team
Integration code owned by you, in your repositories
Evaluation, monitoring and alerting set up and documented
A trained team and a knowledge-transfer handover
A weekly demo and written update — no black-box months
Additional benefits
05 / Process
How we work with you
A phased approach with clear artifacts—so stakeholders see progress weekly, not only at launch.
- 01
Immersion
Access, stakeholder interviews, workflow shadowing and a data review.
- Stakeholder map
- Data access
- Immersion notes
- 02
First value
A working prototype in users' hands on real data.
- Working prototype
- User feedback
- 03
Production
Hardening, integration, security review and launch.
- Production launch
- Security sign-off
- 04
Scale
Adoption tracking, new use cases, and knowledge transfer to your team.
- Adoption report
- Handover docs
06 / Industries
Use cases by industry
Where teams in our focus industries are already applying this service.
Travel & Hospitality
- Embedded engineer taking a booking-agent pilot from sandbox to production across PMS/GDS integrations
Healthcare
- FDE pod building a clinical documentation programme end to end with hospital IT and compliance
EdTech
- FDE embedded with a university team to ship an admissions assistant across term
07 / Stack
Stack & integrations
Representative tools—we meet you where your stack already lives and document every handoff.
Engineering
AI capabilities
Your environment
08 / Considerations
Good to know
How this differs
- Staff augmentation gives you hours; FDEs are accountable for an outcome in production
- Consultants often recommend; FDEs build — most time goes into shipping code
Ownership & security
- You own everything built in your environment, unless agreed otherwise
- FDEs sign your NDAs, use your devices or VDI if required, and follow your access and compliance rules
09 / Engagement
Engagement models
FDE Sprint
1 FDE, 4–6 weeks — one focused use case taken to a production pilot.
FDE Pod
2–3 FDEs + part-time architect, 3–6 months — a multi-use-case AI programme or platform build.
Embedded FDE
1–2 FDEs, 6–12 months, renewable — ongoing AI capability while you build your own team.
Platform partner FDE
1–2 FDEs, per deployment — for AI and SaaS vendors who need FDEs for their customers.
Starter offer
4-Week FDE Sprint
4 weeks · [₹ / $ amount — confirm before publishing]
One senior FDE for 4 weeks. Week 1 immersion, a working prototype by week 3, and a production roadmap in week 4. You get weekly demos, and you can cancel after week 1 if it isn’t the right fit.
10 / FAQ
Frequently asked questions
How is this different from staff augmentation?
How is it different from a consulting project?
Who owns the code?
Can FDEs work under our security policies?
Ready to talk specifics?
Share your goals, timelines, and stack—we will propose a scoped next step.
Often paired with