Custom AI tools built by the same team that diagnosed the opportunity.
Custom AI tool development for mid-market companies. Agentic workflows, automation engines, dashboards, and content pipelines built in 2-week sprints with working demos. 4-16 weeks to production.
The Problem
Most custom AI projects die between the demo and production.
The handoff gap: A strategy firm told you what to build. Now you need someone to build it. The dev shop you're evaluating has never seen the diagnostic, doesn't understand why this use case was prioritized, and prices based on hours. You end up managing two vendors who don't talk to each other, and the thing that ships barely resembles what was recommended.
Proof-of-concept purgatory: You or a vendor built a working prototype. It demoed well. Everyone was excited. That was four months ago. It still isn't in production because nobody planned for authentication, data pipelines, error handling, edge cases, monitoring, or the seventeen integration points with your existing systems. A demo is not a product. The gap between the two is where budgets and timelines balloon.
Off-the-shelf tools that almost work: You've evaluated the SaaS options. They handle 70% of what you need, but the remaining 30% is exactly the part that makes your business different. You're paying for features you don't use, can't customize the ones that matter, and your data sits in someone else's infrastructure. The total cost of ownership for a tool that 'almost works' often exceeds the cost of building exactly what you need.
Internal teams that can't context-switch: Your engineering team is capable, but they're maintaining your core product. Pulling two senior engineers off the roadmap to learn AI tooling, experiment with prompt engineering, and build infrastructure they've never touched will cost you 3-4 months of product velocity. No guarantee they get it right the first time.
Our Approach
Scoped. Sprinted. Shipped. Every build follows a structured process that keeps scope tight and puts working software in your hands early.
Phase 1 — Scoping & architecture (Week 1): We define exactly what's being built, how it connects to your systems, and what 'done' looks like before writing any code. If you've completed our AI Strategy Diagnostic, this phase goes faster because the business case and data audit are already done. For new clients, we run compressed discovery focused on build scope. Output: Technical Architecture Document covering system design, data flows, API integrations, model selection, and a sprint plan. Deliverable: Technical Architecture Document, sprint plan with milestones, success metrics framework, and fixed-scope agreement
Phase 2 — Sprint cycles (Weeks 2-13 (varies by scope)): Two-week sprints, same rhythm every time: planning (day 1), development (days 2-8), internal QA (days 9-10), client demo (day 10) where you interact with working software and give feedback, then adjustments (days 11-14). You never go more than two weeks without seeing progress. Every demo is working software you can interact with. If something goes off track, we catch it in week 2 instead of month 4. Deliverable: Working software increment every 2 weeks, sprint retrospective notes, updated backlog and timeline
Phase 3 — UAT & deployment (Final sprint): The last sprint covers UAT, production deployment, and handoff. Structured UAT sessions with your team, final issue resolution, deployment to your environment (or ours), monitoring and alerting setup, and practical training for daily users. Full documentation included: technical architecture, API docs, runbooks, and a troubleshooting guide your team can use without calling us. Deliverable: Deployed system, complete technical documentation, user training sessions, monitoring and alerting setup
Phase 4 — Post-launch support (2 weeks after deployment (included)): Every build includes two weeks of post-launch support at no extra cost. We monitor performance, fix issues that surface with production usage, tune models based on live data, and make sure your team is confident running the system on their own. We don't hand off until it's working in your environment, with your data, with your users. Deliverable: Performance monitoring report, optimization adjustments, handoff confirmation, optional retainer proposal for ongoing support
Deliverables
Architecture (Week 1)
- Technical Architecture Document with system design, data flows, and integration specs
- Sprint plan with defined milestones, deliverables, and success criteria
- Fixed-scope agreement with transparent pricing
Build (Sprint Cycles)
- Working software demo every 2 weeks — interactive and practical
- Sprint retrospective notes and updated project timeline
- Ongoing access to the development team via a Slack/Teams channel
Deployment & Handoff
- AI system deployed to your infrastructure (or managed hosting)
- Complete technical documentation: architecture, API docs, runbooks
- User training sessions (recorded for future onboarding)
- Monitoring and alerting configuration
- 2 weeks of post-launch support included
Who This Is For
Right for you if: You've identified an AI use case with a validated business case (through our diagnostic or your own analysis) and you're ready to commit budget and executive attention to building it.. You have an internal engineering team but don't want to pull them off the product roadmap to learn AI tooling from scratch. You need a team that can deliver working AI software on a fixed timeline.. You've been burned by a failed AI pilot or a proof-of-concept that never reached production. You want a build partner with process discipline and a track record of shipping..
Not right if: You haven't identified what to build or why. Start with our AI strategy & diagnostic so you don't build the wrong thing expensively.. You're looking for a quick chatbot wrapper or a simple API integration your internal team could ship in a week. Our builds are for systems where business logic, data pipelines, and user experience all matter..
Use Cases
Financial services: A trade finance firm was spending 40+ analyst hours per week on manual document verification across loan applications. Turnaround time was 3 days per application, and error rates climbed as volume grew. — Built an agentic document processing pipeline: automated extraction of key data from bank statements, ITRs, and identity documents using vision models and structured parsing, cross-referenced against internal risk criteria, and routed flagged applications to human reviewers with pre-populated assessment summaries. Integrated with the existing LOS via API.. Outcome: 85% of verification tasks automated. Average processing time dropped from 3 days to 4 hours. Analyst team moved to higher-value underwriting decisions. Estimated annual savings: $150,000.
E-commerce / D2C: A D2C brand at $12M revenue was producing 200+ pieces of content monthly across product descriptions, social media, email campaigns, and blog posts. The content team was bottlenecked, quality was inconsistent, and brand voice drifted with every new freelancer. — Built a custom content pipeline powered by fine-tuned language models trained on the brand's existing high-performing content. The pipeline generates first drafts for product descriptions, social captions, and email sequences in the brand's voice, with human review as the final step. Included a dashboard for the content lead to manage queue, approve drafts, and track output metrics.. Outcome: Content production capacity doubled without additional headcount. First-draft acceptance rate: 72% (up from 30% with the previous freelancer workflow). Time from brief to published: down from 5 days to 1.5 days.
B2B SaaS: A logistics SaaS company with 150+ enterprise clients was drowning in support tickets. First-response time had hit 8 hours, resolution time was over 48 hours, and the support team spent most of their time answering the same 25 questions in slightly different ways. — Built an AI-powered support triage and response system. Incoming tickets are classified by urgency, category, and complexity. For the top 25 recurring question patterns, it generates draft responses from the knowledge base, product docs, and the customer's configuration. Complex tickets go to the right specialist with a pre-populated context summary.. Outcome: First-response time dropped from 8 hours to 23 minutes. Tier-1 ticket volume cut by 58%. Support team moved to customer success and expansion revenue work.
Results
What a build looks like end to end.
Fintech -- document processing automation: 85% reduction in manual verification, 3-day to 4-hour turnaround. A lending platform processing 500+ loan applications per week engaged us after completing our AI Strategy Diagnostic, which identified document verification as the highest-ROI automation opportunity. The scoping sprint took 5 days, faster than usual because the diagnostic had already mapped the workflow, assessed data readiness, and quantified the business case. We built the system in four 2-week sprints: Sprint 1 delivered the core document extraction pipeline for bank statements and ITRs. Sprint 2 added identity verification and cross-referencing against internal risk criteria. Sprint 3 built the reviewer dashboard and exception handling workflows. Sprint 4 focused on integration with the existing LOS, load testing, and production deployment. UAT ran for one week with the underwriting team processing live applications alongside the existing manual workflow. After confirming accuracy and reliability, we cut over fully. Two weeks of post-launch support resolved minor edge cases around unusual document formats. The pipeline now processes 85% of applications without human intervention. The remaining 15% reach a human reviewer with pre-populated assessments, cutting their review time by 60%. Estimated first-year savings: $150,000.
Frequently Asked Questions
How long does a custom AI development project take?
4-16 weeks from kickoff to production, depending on scope. Single-workflow builds: 4-6 weeks. Multi-component systems with integrations: 6-10 weeks. Complex platform-level builds: up to 16 weeks. Every build includes a 1-week scoping phase and 2 weeks of post-launch support. Sprint plan with milestones is published before development starts.
What does the engagement look like week by week?
Week 1 is scoping and architecture: Technical Architecture Document, sprint plan, and success metrics. Week 2 onward, 2-week sprint cycles. Each sprint: planning (day 1), development (days 2-8), internal QA (days 9-10), client demo (day 10), feedback (days 11-14). You interact with working software every two weeks and have direct access to the dev team via Slack or Teams.
What does my team need to provide during the build?
A product owner who can attend sprint demos (90 minutes every 2 weeks), make scope decisions, and give feedback within 48 hours. Access to the systems and APIs we need to integrate with. A UAT team (2-3 people) available during the final sprint. That's it. We handle architecture, development, QA, deployment, and documentation.
What if the project scope changes mid-build?
Expected, and the sprint model handles it. At the start of each sprint, we reassess priorities based on what we learned in the previous one. Small adjustments get absorbed. Major new capabilities get scoped as additional sprints with transparent pricing. We won't silently expand scope and surprise you with an invoice. Every change is discussed, agreed, and documented before work starts.
What if the AI tool doesn't perform as expected?
We define success metrics during scoping: accuracy thresholds, processing times, error rates. Every sprint demo includes performance against those benchmarks. If the system isn't meeting targets by UAT, we keep iterating within scope until it does. The 2-week post-launch support window exists to catch issues that only surface with production data.
How is this different from hiring a freelancer or an offshore dev shop?
Business context: we understand why the tool matters and how it fits your business, especially if we ran the diagnostic. A freelancer builds to spec; we build to outcome. Production discipline: sprint structure, QA, documentation, monitoring, deployment practices most freelancers skip. Continuity: the same senior team runs your project from scoping through post-launch. You're not a ticket in a queue.
Do we own the code and IP?
Yes. Everything we build (source code, documentation, trained models, custom prompts, configurations) is your IP. We keep no license to use, resell, or repurpose your custom work. One exception: we may use anonymized general learnings (not your code or data) to improve our internal processes. Covered in the agreement.
Can you work with our existing tech stack?
Yes. Our default stack is Python/FastAPI or Node.js, React/Next.js, PostgreSQL, and AWS/GCP, but we adapt. Azure, MongoDB, Java backend -- we integrate with what you have. For healthcare: EHR systems via HL7v2 and FHIR R4. For manufacturing: MES, SCADA, and historian data systems. Scoping maps your infrastructure and defines the integration approach before development starts.
