Industry
Information Technology Services & Consulting
Company type
Mid-size software services firm (150–500 employees)
Team
Pre-sales, Solutions Architects, Account Managers
Outcome period
90 days post-deployment
The Challenge
Pre-sales teams at software services firms spend a disproportionate share of their productive hours on proposal creation — a process that’s largely manual, inconsistently structured, and hard to scale. Industry data reflects this clearly:
| IT industry benchmark | Typical value |
|---|---|
| Average time to create one technical proposal | 12–18 hours |
| Time-to-send from initial client conversation | 4–7 business days |
| Proposal win rate (IT services, global average) | 22–28% |
| Cost per proposal (SA time + coordination) | $800–$2,200 |
| Proposals a solutions architect can handle per month | 4–6 |
| Rework rate due to estimation errors | 35–45% |
| Stakeholder alignment cycles per proposal | 2–4 rounds |
The firm’s pre-sales team was operating at or below these benchmarks. Key friction points:
- No standardized structure — each architect built proposals differently, leading to inconsistent quality and missed sections
- Estimation guesswork — hours were estimated informally without structured complexity analysis, leading to frequent under-scoping
- Slow turnaround — client-ready documents took 5–7 days from first conversation to delivery, often losing deals to faster competitors
- Repetitive effort — common artefacts (stakeholder maps, module breakdowns, user stories, cost models) were recreated from scratch for every engagement
- No institutional memory — past projects weren’t referenced systematically when scoping new work
Features:
- Paste a call transcript — platform auto-extracts requirements, budget, timeline, tech stack, and compliance needs
- Eliminates manual requirements documentation from the workflow entirely
- Each of the 8 workflow stages triggers purpose-built AI generation for its artefacts
- Solution components, user stories, user flows, screen specs, team composition, cost strategy
- Architects review and refine AI-generated content — never start from blank
- Complexity factors, risk buffers, and screen counts derived from the project’s own module and story data
- Not generic rules of thumb — estimation logic tied to each engagement’s specifics
- Rework rate dropped from ~40% to ~12%
- Comparable past engagements surfaced during review
- Architects get reference points for pricing, timeline, and risk without searching shared drives
- Fully structured proposal generated directly from workflow data: executive summary, scope, timeline, cost breakdown, commercial terms
- Client receives secure approval link by email; acceptance/rejection captured and logged
- Every session logged: total sessions, average session length, longest session
- Full audit trail of pre-sales time invested per engagement
Technology
The Solution:
The firm deployed an AI-assisted proposal management platform structured around an 8-stage pre-sales workflow — from the first client conversation through to digital sign-off.
Rather than replacing the architect, the platform acts as a co-pilot: capturing client context in Stage 1, generating solution architecture and module breakdowns in Stage 3, producing estimation logic in Stage 6, and assembling a client-ready PDF in Stage 7 — all within a single guided environment.
Key capabilities:
- Transcript-to-proposal in one step — paste a call transcript and the platform extracts requirements, budget, timeline, tech stack, and compliance needs automatically
- Structured AI generation — each stage triggers purpose-built AI generation for its artefacts: solution components, user stories, user flows, screen specs, team composition, and cost strategy
- Intelligent estimation — complexity factors, risk buffers, and screen counts are derived from the project’s own module and story data, not generic rules of thumb
- Past-project similarity matching — the platform surfaces comparable past engagements during review, giving architects reference points for pricing and scoping
- Time tracking per proposal, per stage — every session is logged; the platform shows total hours invested by the pre-sales team per engagement
- One-click PDF output — a fully structured proposal document is generated directly from the workflow data, including executive summary, scope, timeline, cost breakdown, and commercial terms
- Digital sign-off workflow — clients receive a secure approval link by email; acceptance or rejection is captured and logged
Services Offered
- AI Product Design & Workflow Architecture
- Transcript Parsing & NLP Pipeline
- 8-Stage Pre-Sales Platform Development
- Estimation Engine Development
- PDF Generation & Digital Sign-off
- Past-Project Similarity Matching
- Ongoing AI Model Optimisation
Results After 90 Days
Time Efficiency (Pre-Sales Team Hours)
| Metric | Before (self-reported) | After (system-tracked) | Change |
|---|---|---|---|
| Avg. hours per proposal | 14.2 hrs | 3.8 hrs | −73% |
| Time-to-send (days) | 5.4 days | 1.6 days | −70% |
| Proposals per SA per month | 4–5 | 12–15 | +3x throughput |
| Rework rate | ~40% | ~12% | −70% |
| Estimation confidence (architect-rated) | Low / Medium | Medium / High | Significant lift |
Note on time tracking: the platform records total time spent per proposal in seconds, broken down by session — total sessions, average session length, longest session. Figures above are derived from this data, aggregated across the team over the measurement period.
Quality Improvements
- Completeness: proposals generated through the platform covered all required sections 100% of the time vs. ~55% for manually created documents
- Estimation accuracy: projects scoped through the platform’s structured estimation flow came within ±15% of actual delivery hours in 7 of 10 cases (vs. ±40% historically)
- Stakeholder coverage: AI-generated stakeholder maps identified 3–6 distinct business roles per engagement, ensuring proposals addressed the full buying committee rather than only the technical contact
- Consistency: section structure, terminology, and financial presentation were uniform across all proposals regardless of which architect created them
Business Impact
| KPI | Before | After | Change |
|---|---|---|---|
| Proposal win rate | 24% | 38% | +14 pts |
| Avg. deal size (closed proposals) | $185K | $240K | +30% |
| Time SA spent on admin vs. client work | 60% / 40% | 25% / 75% | Inverted |
| Proposals sent per month (team total) | 18 | 52 | +189% |
IT Industry Benchmarks: Efficiency Reference
| Benchmark | Industry average | World-class target | Platform outcome |
|---|---|---|---|
| Hours per proposal | 12–18 hrs | < 4 hrs | 3.8 hrs |
| Time-to-send | 4–7 days | < 2 days | 1.6 days |
| Proposals per SA per month | 4–6 | 12+ | 12–15 |
| Win rate | 22–28% | 35%+ | 38% |
| Estimation error margin | ±35–45% | ±15% | ±15% |
| Rework rate | 35–45% | < 15% | ~12% |
Solutions Architect Time Allocation
Before deployment (self-reported averages):
Research & requirements gathering
3.5 hrs
Writing solution description & modules
4.0 hrs
Estimation & team composition
3.0 hrs
Costing & commercial terms
2.0 hrs
Document formatting & PDF assembly
1.7 hrs
Total
14.2 hrs
After deployment (system-tracked averages):
Client intake (Stage 1 — AI transcript analysis)
0.5 hrs
Solution review & editing (Stage 3 — AI-generated base)
1.0 hrs
Estimation review (Stage 6 — AI-generated base)
0.6 hrs
Costing & terms (Stage 7 — AI-generated base)
0.5 hrs
Final review, PDF generation, sign-off setup
0.4 hrs
Coordination / overhead between stages
0.8 hrs
Total
3.8 hrs
Time freed per proposal: ~10.4 hours — redirected to client engagement, discovery calls, and pipeline expansion.
Solutions Architect Time Allocation
01
Starting from a structured base, not a blank page
Rather than building a proposal from scratch, architects reviewed and refined AI-generated content. The cognitive load shifted from creation to validation — a fundamentally faster mode of work.
02
Estimation grounded in actual project data
The estimation engine derives hours from the project’s own module breakdown, story counts, and complexity factors — not gut feel or generic tables. Architects reported higher confidence in their numbers from day one.
03
Institutional memory surfaced automatically
Similar past projects were surfaced during the review stage, giving architects reference points for pricing, timeline, and risk — without searching shared drives or asking colleagues.
What This Tells You About Our AI Engineering
The specific build here was an 8-stage pre-sales workflow. The underlying capability — transcript-to-structured-data extraction, purpose-built AI generation at each workflow stage, estimation logic grounded in real project data, and a fully tracked, auditable system — applies well beyond proposals. We bring the same approach to:
- Operational workflows buried in manual, repetitive document or data work
- Processes where institutional knowledge lives in people’s heads rather than systems
- Teams that need AI to concentrate human judgement, not replace it
- Organizations that need a production system, not a proof of concept
We’ve applied this kind of AI-implementation work across fintech, edtech, healthcare IT, logistics, and enterprise SaaS engagements — proposals, recruitment, and well beyond either.
What This Demonstrates
This case study isn’t a proposal tool we’re trying to sell you — it’s evidence of how we build AI-implemented applications: an 8-stage guided workflow with transcript parsing, structured AI generation, and estimation logic grounded in real project data, validated against 90 days of production use. If you’re evaluating an AI partner for a different problem entirely — not pre-sales, not proposals — this is the engineering and AI-implementation depth you’d be working with.
Book a 30-minute Digital Transformation & Product Roadmap Review with Carmatec’s team to talk through what an AI-implemented application could look like for your own operations. We’ve been building software and AI systems for 23 years, across teams in Bangalore, Doha, Dubai, New York, and London.
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